Kalyan Perumalla

Career In Brief

Sat, 01 Mar 2025 00:00:00 +0000

View a brief overview of Dr. Perumalla’s professional career path

DarkNet Cyber Resilience

Tue, 22 Mar 2022 00:00:00 +0000

DarkNet Cyber Resilience investigates the impacts of cyber stress and its thresholds of impact on alternative timing signals for the US national energy grid.

Cyber Resilience of Alternative Timing for Energy Grids

Overview

This project is focused on performing a systematic evaluation of the impacts of cyber stress on the system. It is developing tools that enable early detection of performance issues and rapid recovery from cyber-related events. A unique advancement of the project lies in going beyond general cybersecurity vulnerabilities and exposures (CVE). This is achieved by evaluating their implications to the cyber resilience specifically with regard to precise effects on the transmission and receipt of timing signals over the complex networks connecting the timing sources to the geographically-distant subscribers.

Key Links

DarkNet Website

Gallery

Organization

Sponsor: US Department of Energy (DOE)
- Office: Office of Electricity (OE)
- Program: DarkNet
Prime: Oak Ridge National Laboratory (ORNL)

Selected Publications

DarkNet Cyber Resilience

Two key cyber elements affecting the grid’s timing capability are Cyber Resilience and Cyber Trust. The timing services of DarkNet are systematically subjected to a range of cyber phenomena that stress four key performance factors, namely: Accuracy, Manageability, Telemetry, and Visibility. This analysis is designed to provide insights into four important categories of undesirable cyber phenomena: Loss of View (LoV), Loss of Control (LoC), Manipulation of View (MoV), and Manipulation of Control (MoC).

Kalyan Perumalla, Juan Lopez

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CYVET

Sat, 01 Jan 2022 00:00:00 +0000

Our new Cyber-Physical Security Assurance Framework based on Semi-Supervised Vetting applies the latest AI/ML and NLP technologies on hardware testbeds to advance the resilience of critical cyber-physical assets including electric grids and gas pipelines.

CYVET Pipeline

Overview

Organization

Sponsor: US Department of Energy (DOE)
- Office: Cybersecurity, Energy Security, and Emergency Response (CESER)
- Program: Cybersecurity for Energy Delivery Systems (CEDS)
- Award: CESER
Prime: Oak Ridge National Laboratory (ORNL)
- Subcontract: University of Nebraska-Lincoln (UNL)
Period: 2019-2023

Gallery

OSTI.gov: https://www.osti.gov/servlets/purl/1661247

CyBERT: Cybersecurity Claim Classification by Fine-Tuning the BERT Language Model

We introduce CyBERT, a cybersecurity feature claims classifier based on bidirectional encoder representations from transformers and a key component in our semi-automated cybersecurity vetting for industrial control systems (ICS)…The results showed that CyBERT outperforms these models on the validation accuracy and the F1 score, validating CyBERT’s robustness and accuracy as a cybersecurity feature claims classifier.

Kimia Ameri, Michael Hempel, Hamid Sharif, Juan Lopez, Kalyan Perumalla

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Trust-but-Verify in Cyber-Physical Systems

Cyber-physical systems span a wide spectrum, from long-lived legacy systems to more modern installations. Trust is an issue that arises across the spectrum, albeit with different variants of goals and constraints. On the one end of the spectrum, legacy systems are characterized by function-based designs in which trust is an implicitly in-built concept…

Kalyan Perumalla

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Smart Semi-Supervised Accumulation of Large Repositories for Industrial Control Systems Device Information

A solution is needed for vetting the vendor-supplied feature claims and their adherence to cybersecurity requirements and standards. We are presently engaged in an effort to develop such a system. This paper demonstrates one vital aspect of this effort in proposing an end-to-end framework to accumulate a large repository of ICS device information for this vetting system, curate the dataset, and conduct extensive processing. This framework is designed to use web scraping, data analytics and Natural Language Processing (NLP) techniques to identify vendor websites, automate the collection of website-accessible documents and automatically derive metadata from them for identification of product documents relevant to the repository…

Kimia Ameri, Michael Hempel, Hamid Sharif, Juan Lopez, Kalyan Perumalla

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A Novel Vetting Approach to Cybersecurity Verification in Energy Grid Systems

The cybersecurity auditing for Operation Technology is critical and has been largely missing from the cybersecurity research, especially in the energy sector. In this paper, we present a novel “cybersecurity vetting” approach (CYVET) to the problem of verification and validation of cybersecurity in complex cyber-physical installations underlying modern energy grid systems.

Kalyan Perumalla, Juan Lopez, Maksudul Alam, Olivera Kotevska, Michael Hempel, Hamid Sharif

PDF DOI

Additional Background

The cybersecurity auditing for Operation Technology (OT) is critical and has been largely missing from the cybersecurity research, especially in the energy sector. CYVET is a novel “cybersecurity vetting” approach (CYVET) to the problem of verification and validation of cybersecurity in complex cyber-physical installations underlying modern energy grid systems.

In Information Technology (IT), cybersecurity auditing is a widespread practice to ensure privacy, security, and trust. However, for the field of Operation Technology (OT) as used in electric energy systems, this is a relatively novel concept. In fact, OT itself only recently began to embrace IT principles, with the push for automation and centralized control driving this development. OT operators are simply not yet used to the idea of cybersecurity. To ameliorate the gap, product vendors for field devices are advancing the field by incorporating more and more security features into their products. However, customers are often either unaware of them, or do not use them, or cannot use them because of unsatisfied device ecosystem dependencies. There is thus a disconnect between what is offered, what is possible post-deployment, and what the customer expects.

There is a vast lack of cybersecurity oversight and insight, from a certification and a customer perspective alike, for OT systems in the energy sector. With new features constantly being added to new and existing products, customers are predominantly unaware what their purchased solutions are capable of, or not capable of. They often do not know if their current systems meet their own cybersecurity requirements as well as industry standards. Many of these facets not only indirectly depend on device capabilities, but also on device deployment decisions – Does a newly added feature work in an existing context? Can it be used as envisioned? Does it interfere with other cybersecurity requirements? Does it produce side effects that may interfere with other requirements?

Hence, what is needed is a security vetting system designed to provide insight into deployed systems, the match of capabilities to requirements, adherence to certification requirements, and so forth. There are few systems currently available that provide these energy grid security capabilities. OT systems are increasingly cyber-enabled, increasingly complex, and increasingly interdependent. This rapidly accelerating trend poses a clear risk for asset owners to lose confidence and for cybersecurity risk to go undiscovered until exploited by malicious parties.

Professional Biography

Sat, 01 Mar 2025 00:00:00 +0000

View Dr. Perumalla’s professional biography

ASCR CS PI Meeting

Mon, 05 Feb 2024 00:00:00 +0000

CS PI Meeting Annual Computer Science Principal Investigators’ (PI) Meeting held in Atlanta, GA, USA

2024 DOE ASCR CS PI Meeting

Organization

Sponsors: US Department of Energy (DOE)
- Office: Office of Science (SC)
- Programs: Advanced Scientific Computing Research (ASCR)

Gallery

summary: 2024 CS PI Meeting, Atlanta, GA, USA

Frontiers in Big Data - Networks

Mon, 28 Mar 2022 00:00:00 +0000

Dr. Perumalla is an Associate Editor for the international journal Frontiers in Big Data - Networks.

Frontiers in Big Data - Networks

Dr. Perumalla joined the editorial board as Associate Editor for the section Networks under the Big Data.

His overview on the journal website can be found here.

Deep CYBERIA

Sat, 01 Jan 2022 00:00:00 +0000

Deep CYBERIA is a novel system focused on Detecting Sensors Deeply Embedded in Cyber-Physical Systems via novel machine learning and passive/active/hybrid probing techniques on complex operational technology (OT) networks.

Overview

Deep CYBERIA is designed to address the critical capability gap identified by USAF in discovering, identifying and mapping the edge devices and physical sensors connected to those edge devices. The effort is aimed at the outcome of providing situational awareness of Industrial Control Systems/Supervisory Control and Data Acquisition (ICS/SCADA) traffic and devices operating in a network. The situational awareness will support the requirement of performing deep dive filtering analysis and enumeration of ICS traffic.

Demonstration

Organization

Sponsor: US Department of Defense
Prime: Oak Ridge National Laboratory (ORNL)
- Subcontract: MIT Lincoln Laboratory (MIT-LL)
Period: 2019-2025

Gallery

Trust-but-Verify in Cyber-Physical Systems

Kalyan Perumalla

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Detecting Sensors and Inferring their Relations at Level-0 in Industrial Cyber-Physical Systems

In this paper, we present our research and development efforts aimed at addressing the gap in discovering sensors at level 0 in industrial CPS by building a system called Deep-cyberia (Deep Cyber-Physical System Interrogation and Analysis) that incorporates algorithms and interfaces aimed at uncovering sensors and computing estimates of correlations among them.

Kalyan Perumalla, Srikanth Yoginath, Juan Lopez

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Volatile Memory Extraction-Based Approach for Level 0-1 CPS Forensics

Our focus is to extract volatile and dynamically changing internal information form CPS 0-1 level devices, and design preliminary schemes to exploit that extracted information. As a case study, we apply the proposed methodology to Modicon PLC using Modbus protocol. We extract the memory layout and subject the device to read operations at the most critical regions of memory. This capability of generating a sequence of volatile memory snapshots for offline, detailed and sophisticated analysis opens a new class of cyber security schemes for CPS forensic analysis, taint analysis and watermarking.

Rima Asmar Awad, Juan Lopez, Michael Rogers, Kalyan Perumalla

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Areas of Interest

Sat, 01 Mar 2025 00:00:00 +0000

View Dr. Perumalla’s areas of interest

PADS'22 General Co-Chair

Wed, 08 Jun 2022 00:00:00 +0000

Dr. Perumalla served as a General Co-Chair of the ACM SIGSIM PADS'22 Conference.

PADS'22

Exascale Computing for the National Energy Grid

Sat, 01 Jan 2022 00:00:00 +0000

Exascale Computing for the Stochastic Grid Dynamics of the US national energy transmission network is a part of the Exascale Computing Project to tap the world’s largest supercomputer to solve the nation’s energy grid problems.

Exascale Computing Project

Overview

The Exascale Computing Project that enables US revolutions in technology development: scientific Discovery; health care; and energy, economic, and national security. Exascale computing will provide the capability to tackle challenges at levels of complexity and performance that previously were out of reach.

The Problem

How can we secure the national energy transmission network despite unexpected cyber attacks, natural disasters, and unpredictable load fluctuations?
How fast and far into the future can we make it resilient?
How can we ultimately achieve this at the least cost to everyone?

The Solution

Enter Exascale computing and Global Optimization.

This project attacks this nationally important problem with an unprecedented high-technology approach that relies on supercomputing to work out the very best global solutions on-the-fly, rapidly examining millions of configurations involving all the energy generators, transmission lines, event contingencies, and complex physical constraints. Very high-end algorithms implemented with sophisticated parallel processing software is designed for the state-of-the-art supercomputing hardware, combining the know-how of some of the very best minds across the US national laboratory systems, tapping a range of experts in energy domain sciences, computational optimization, numerical algorithms, high-end computing hardware, and advanced software stack organization techniques.

Organization

Sponsor: US Department of Energy (DOE)
- Office: Advanced Scientific Computing Research (ASCR)
- Program: Exascale Computing Project (ECP)
Institutions: PNNL, ORNL, ANL, LLNL, NREL
Period: 2019-2023/24

Abbreviations

PNNL = Pacific Northwest National Laboratory
ORNL = Oak Ridge National Laboratory
ANL = Argonne National Laboratory
LLNL = Lawrence Livermore National Laboratory
NREL = National Renewable Energy Laboratory
PI = Principal Investigator

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ReveR-SES: Reversible Software Execution Systems

Sat, 01 Jan 2022 00:00:00 +0000

Reversible Software Execution Systems ReveR-SES is a paradigm shift in ultra-scale computing to address the outstanding scaling challenges by enabling an entirely new, orthogonal dimension to all aspects of traditional, forward-only computing.

ReveR-SES

Overview

ReveR-SES not only provides expeditious, novel solutions to scaling problems, but also opens new, longer-term research and development directions in high performance computing.

All traditional computing is done forward-only, but never in reverse order. Only recently it has been discovered that executing codes backwards can be used to greatly increasing the efficiency and usability of high performance computing. However, rendering a program reversible is an extremely challenging endeavor. This project is focused on developing the methodologies to exploit reversibility to enable scaling applications to ultra-scale platforms with 1,000,000 processor cores.

ReveR-SES contains several novel ideas, including reversible compilers, reversible libraries, reversibility extensions to standard interfaces, as well as relation to thermodynamics, information and entropy.

Quad chart

ReveR-SES Confluence

The reversible execution paradigm represents an entirely new research direction, yet, has immediate relevance to existing DOE applications and also the potential for creation of entirely new technologies and the creation of new high-technology jobs in computing. This is due to the orthogonality of reversibility to many existing HPC dimensions.

ReveR-SES Components

Overall, the new execution paradigm provides energy savings in the short-term and better energy-efficient designs in the longer term. The reversible execution systems are directly relevant to important applications such as climate, plasma physics, and materials science simulations.

ReveR-SES was Dr. Kalyan Perumalla’s Early Career Research project 2010-2015, awarded as a single-principal investigator, $2.5 million project.

https://science.osti.gov/ascr/Community-Resources/ECRP-Awardees

Scope

To define, develop, test, and implement the paradigm of reversible software execution for exascale computing.

Primary Scientific Thrusts

Developing fundamentally new reversible computer arithmetic and logic
Designing efficient asynchronous rollback-based recovery via reversible execution
Redesigning traditional physical system models to enable reversible simulation
Reevaluating theoretical computational and energy consumption interplay.

Science Impacts

Enables new capabilities for computational science common to many areas

Extremely efficient method for fault tolerant simulations on next generation heterogeneous (CPU+GPU) systems
Solves the synchronization problem at very large scales of concurrency
Overcomes undesirable reliance on memory
- Addresses the exascale hardware problem of high ratio of computational speed to memory speed
- Computational energy reduced via reduced memory footprint
Provides the most promising approach to debugging at exascale.

Enables Theoretical Advancements

Directly relates energy of computation to computational model characteristics
Positions scientific simulations for future reversible computing hardware (adiabatic circuits, Quantum Computing).

Gallery

Organization

Sponsor: US Department of Energy (DOE)
- Office: Advanced Scientific Computing Research (ASCR)
- Program: Early Career Research Program (ECRP)
Prime: Oak Ridge National Laboratory (ORNL)

Selected Publications

Normalcy, Magic, Miracle and Error: Emergence along a Reversibility Spectrum

Formation of a butterfly from a pupa, extraction of a live dove from a magician’s empty hat, generation of new particles from high-energy particle collisions and spawning a new dream world from mind in sleep are all examples of a common, fuzzy notion called ‘emergence’. In this paper, I pin the concept of emergence to the element of surprise in a phenomenon. I categorise the various notions of emergence into three main classes. These definitions are used to explain instances of emergence, organised along a continuous spectrum as normality, magic, miracle and error.

Kalyan Perumalla

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Efficient reversible uniform and non-uniform random number generation in UNU.RAN

Reversible random number generations are useful in large-scale fault-tolerant parallel computations and parallel discrete event …

Srikanth Yoginath, Kalyan Perumalla

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Towards Reversible Basic Linear Algebra Subprograms: A Performance Study

Problems such as fault tolerance and scalable synchronization can be efficiently solved using reversibility of applications…

Kalyan Perumalla, Srikanth Yoginath

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Reverse Computation for Rollback-based Fault Tolerance in Large Parallel Systems

Reverse computation is presented here as an important future direction in addressing the challenge o…

Kalyan Perumalla, Alfred Park

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Tutorial: Introduction to Reversible Computing

This tutorial provides an introduction to the concept of reversible computing, adopting an expanded view…

Kalyan Perumalla

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Reversibly Finding the Square Root of an Integer

Here we consider a case study in reversible computing, namely, how to reversibly compute the integer square root …

Melissa Yu, Kalyan Perumalla

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Reversible Simulations of Elastic Collisions (Extended arXiv version)

Consider a system of N identical hard spherical particles moving in a d-dimensional box and undergoing elastic, possibly multi-particle, collisions…

Kalyan Perumalla, Vladimir A. Protopopescu

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Reversible Simulations of Elastic Collisions

Consider a system of N identical hard spherical particles moving in a d-dimensional box and undergoi…

Kalyan Perumalla, Vladimir A. Protopopescu

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Introduction to Reversible Computing

Few books comprehensively cover the software and programming aspects of reversible computing. Fillin…

Kalyan Perumalla

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ReveR-SES: Reversible Software Execution Systems

[Pub 137]

Kalyan Perumalla

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Discrete Event Execution and Reversibility: Challenges in the Path to Asynchrony for Massively Parallel Computing

To keep up with the increasing number of processing elements in parallel/distributed computing, traditional tightly-coupled time-stepped models…

Kalyan Perumalla

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Reversible Parallel Discrete Event Formulation of a TLM-based Radio Signal Propagation Model

Radio signal strength estimation is essential in many applications, including the design of military…

Sudip Seal, Kalyan Perumalla

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ReveR-SES: Reversible Software Execution Systems

[Pub 136] http://science.energy.gov/ascr/ascac/meetings/nov-2011/

Kalyan Perumalla

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Reversible Parallel Discrete-Event Execution of Large-scale Epidemic Outbreak Models

The spatial scale, runtime speed, and behavioral detail of epidemic outbreak simulations altogether …

Kalyan Perumalla, Sudip Seal

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Compiler-based Automation Approaches to Reverse Computation

Automation is useful to facilitate reverse code generation from normal code. Here, we describe our …

Kalyan Perumalla, Christopher Carothers

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Reversible Discrete Event Formulation and Optimistic Parallel Execution of Vehicular Traffic Models

Vehicular traffic simulations are useful in applications such as emergency planning and traffic mana…

Kalyan Perumalla, Srikanth Yoginath

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Perfect Reversal of Rejection Sampling Methods for First-Passage-Time and Similar Probability Distributions

We present a perfectly reversible method for bi-directional generation of samples from computational…

Kalyan Perumalla, Aleksandar Donev

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Affiliations

Sat, 01 Mar 2025 00:00:00 +0000

View a list of Dr. Perumalla’s institutional affiliations

ACM SIGSIM Chair

Sat, 01 Jan 2022 00:00:00 +0000

Dr. Perumalla is the elected Chair of ACM Special Interest Group on Simulation and Modeling (ACM SIGSIM), the worldwide professional body dedicated to all advanced areas in simulation and modeling.

ACM SIGSIM

Dr. Perumalla serves as the elected Chair of ACM SIGSIM for the term 2020–2024.

ACM TOMACS

Sat, 01 Jan 2022 00:00:00 +0000

Dr. Perumalla is an Associate Editor with the Association for Computing Machinery (ACM) Transactions on Modeling and Computer Simulation (TOMACS), which provides a single archival source for the publication of high-quality research and developmental results in all phases of the modeling and simulation life cycle.

Association for Computing Machinery (ACM) Transactions on Modeling and Computer Simulation

Dr. Perumalla serves on the Editorial Board of ACM TOMACS as an Associate Editor. He has been serving since 2008 to date, covering various aspects of discrete modeling and simulation, with particular focus on parallel and distributed methods.

The subjects of emphasis of ACM TOMACS are discrete event simulation, combined discrete and continuous simulation, as well as Monte Carlo methods. The use of simulation techniques is pervasive, extending to virtually all the sciences.

TOMACS serves to enhance the understanding, improve the practice, and increase the utilization of computer simulation. Submissions should contribute to the realization of these objectives, and papers treating applications should stress their contributions vis-á-vis these objectives.

Journal of Simulation

Sat, 01 Jan 2022 00:00:00 +0000

Dr. Perumalla is an Associate Editor with the Journal of Simulation (JOS), which aims to publish both articles and technical notes from researchers and practitioners active in the field of simulation.

Journal of Simulation

Dr. Perumalla serves on the Editorial Board of JOS as an Associate Editor, primarily handling discrete event modeling, agent-based simulation, and parallel and distributed techniques, applied to all domains.

In JOS, the field of simulation includes the techniques, tools, methods and technologies of the application and the use of discrete-event simulation, agent-based modelling and system dynamics. We are also interested in models that are hybrids of these approaches. JOS encourages theoretical papers that span the breadth of the simulation process, including both modelling and analysis methodologies, as well as practical papers from a wide range of simulation applications in domains including, manufacturing, service, defence, health care and general commerce. JOS will particularly seek topics that are not “mainstream” in nature but interesting and evocative to the simulation community as outlined above.

Particular interest is paid to significant success in the use of simulation. JOS will publish the methodological and technological advances that represent significant progress toward the application of simulation modelling-related theory and/or practice.

Other streams of interest will be practical applications that highlight insights into the contemporary practice of simulation modelling; articles that are tutorial in nature or that largely review existing literature as a contribution to the field, and articles based on empirical research such as questionnaire surveys, controlled experiments or more qualitative case studies.

Shorter technical notes will also be considered.

Authors can choose to publish gold open access in this journal.

NAERM

Sat, 01 Jan 2022 00:00:00 +0000

North American Energy Resilience Model provides the next generation planning infrastructure for the US national energy grid using a cloud-based real-time, interoperable, and federated modeling, simulation, and visualization technology.

Overview

NAERM dependencies across energy infrastructure layers

Interactive Contingency Selection

Contributions

As ORNL lead for the NAERM Software Architecture Group, my efforts were focused on enabling cloud-based federated simulations executing in a distributed fashion across PNNL, ORNL and ANL servers. In particular, a dynamically configured and launched network of virtual machines via Amazon Web Services (AWS), coordinated using AWS Step Functions. Automated launches of electric transmission grid scenarios can be triggered in a highly responsive fashion. The software architecture was designed to evaluated using the Siemens PSS/E simulator instances that were licensed to execute in on-premise mode only at ORNL. Additionally, PowerWorld simulations were dynamically interoperated with other instances using federated simulation interfaces of the HELICS system for interfacing with gas network outage models simulated at ANL.

A contingency visualization, selection and launch tool was developed with one of my team members (Dr. Maksudul Alam).

Architecture

Cloud-based Software Architecture for Federated Simulation

Demo Videos

Description

The NAERM will ultimately provide real-time situational awareness and analysis capabilities for emergency events for optimal operations and recovery, so that the Federal Government can quickly and effectively prepare and respond, for example, providing recommendations in coordination with State and local governments, Federal Emergency Management Agency (FEMA), and the National Guard. While the primary focus is on the energy sector, the NAERM will further assist industry in assessing the resilience implications of energy planning decisions on associated infrastructure. NAERM capabilities will also be leveraged by DOE’s National Nuclear Security Administration (NNSA), the Department of Defense (DoD), and the Department of Homeland Security (DHS) in support of their national security missions.
– NAERM Report

The North American Energy Resilience Model project is focused on developing the next generation planning infrastructure for the US national energy grid using a cloud-based real-time, interoperable, and federated modeling, simulation, and visualization technology

Ultimately, NAERM will enable DOE’s provision of situational assessment advice to industry and government to ameliorate the risk of and consequence associated with large- scale service disruptions across infrastructure sectors—and geographic and organizational boundaries—and the lengthy restoration and recovery operations following an extreme event. The NAERM will advance the state-of-science in planning and operations of energy supply in extreme events and provide rigorous resilience and associated economics metrics for these sectors.
– NAERM Report

Organization

Sponsor: US Department of Energy (DOE)
- Office: Office of Electricity (OE)
- Program: North American Energy Resilience Model (NAERM)
Team: ORNL, PNNL, LLNL, ANL, and others

Introduction to Reversible Computing

Sat, 01 Jan 2022 00:00:00 +0000

ISBN 978-1439873403, 1^st Edition | Chapman and Hall/CRC | Taylor&Francis | Amazon
One of the first books on the topic, it contains a compendium of both classical and recently developed results on reversible computing.

About

Introduction to Reversible Computing

This seminal book on reversible computing collects scattered knowledge into one coherent account. It offers an expanded view of the field that includes the traditional energy-motivated hardware viewpoint as well as the emerging application-motivated software approach. It explores up-and-coming theories, techniques, and tools for the application of reversible computing. The topics covered span several areas of computer science, including high-performance computing, parallel/distributed systems, computational theory, compilers, power-aware computing, and supercomputing.

Key Features

Emphasizes the software, programming, application, and usage aspects of reversible computing
Helps readers easily understand complex theoretical and seminal results at a level suitable for senior undergraduate or graduate students
Illustrates the development of reversible code generation using actual code segments in the C language
Provides pseudocodes of several algorithms for memory-less or memory- efficient reversibility, including reversible random number generation and reversible numerical computation
Includes a comprehensive bibliography and resources for further reading

Selected Contents

INTRODUCTION: Scope. Application Areas. The Reversible Computing Spectrum.
THEORY: Systems and Principles. Reversibility-Related Paradoxes. Theoretical Computing Models. Relaxing Forward-Only Execution into Reversible Execution.
SOFTWARE: Reversible Programming Languages. Adding Reversibility to Irreversible Programs. Reverse C Compiler. Reversal of Linear Codes. Reversible Random Number Generation. Reversible Memory Allocation and Deallocation. Reversible Numerical Computation. Reversing a Sorting Procedure. Implementing Undo-Redo-Do.
HARDWARE: Reversible Logic Gates. Reversible Instruction Set Architectures.
SUMMARY: Future Directions.
REFERENCES: Bibliography. Index.

Reference

Introduction to Reversible Computing

Few books comprehensively cover the software and programming aspects of reversible computing. Fillin…

Kalyan Perumalla

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SIMULATION: Transactions of TSMSI

Sat, 01 Jan 2022 00:00:00 +0000

Dr. Perumalla is an Associate Editor with the SIMULATION: Transactions of The Society for Modeling and Simulation International, a refereed journal devoted to advancing the discipline and profession of modeling and simulation.

SIMULATION: Transactions of The Society for Modeling and Simulation International

Dr. Perumalla serves on the Editorial Board of SIMULATION as an Associate Editor.

An archival journal in both print and electronic form, it consists of distinct sections–one devoted to theory, the other to applications. Published articles must have a clear relevance to general modeling and simulation issues. In addition to its archival mission, the journal aims to help professionals and researchers, particularly those involved in multidisciplinary projects, apply advances in modeling and simulation theory, methodology and technology to their application areas.

Methodology papers should be original articles of lasting value dealing with contributions to the modeling and simulation field that are methodological in nature. Areas of interest include, but are not limited to: model execution (e.g., parallel or distributed simulation); simulation interoperability; modeling techniques, languages, and development systems; analysis methodologies and techniques; verification, validation and accreditation; randomness in simulations, e.g., random number generation; techniques for real-time simulation, e.g., human-in-the-loop, hardware-in-the-loop, or on-line simulation. Purely mathematical, computational, or empirical results, however valuable, are considered more appropriate to the many journals specializing in these areas. General approaches, formalisms, algorithms, or techniques should preferably be illustrated with significant applications that demonstrate their applicability to real-world problems.

Applications papers are original articles describing mature applied work involving computational accounts of modeling and simulation. As this is a modeling and simulation journal, application papers contain a detailed description of the modeling and simulation methods employed. The work advance the state of the art in modeling and simulation in general, or the use of modeling and simulation in a specific application area. Proposals for new ways of looking at modeling and simulation include demonstrations of effectiveness. The methods and applications of modeling and simulation in both well-established and emerging areas are included. Application areas of interest include, but are not restricted to:

Computer Science: Computer networking and communications, high performance computers, real-time systems, mobile and intelligent agents, simulation software, and language design.
Engineering: System engineering and design, aerospace, air and surface transportation systems, microelectronics, mechatronics, manufacturing systems, chemical engineering, and robotics.
Natural and Life Sciences: Chemistry, physics, biology, ecology, medicine, and biomedicine.
Military: modeling and simulation for training, analysis, acquisition.
Social and Cognitive Sciences: economics, demography, politics, human behavior, and cognition.

Computational Epidemiology

Tue, 26 Apr 2022 00:00:00 +0000

Global Pervasive Computational Epidemiology

Computational Epidemiology

Overview

NSF Expeditions Project

Organization

Sponsor: NSF
Institutions: University of Virginia
Period: 2020-2025

ZeroIn

Sat, 01 Jan 2022 00:00:00 +0000

Our formulation of a new AI/ML-based, real-time computational framework aims to learn and flag defects in software as early as the time of commit in the developers’ repositories.

ZeroIn Network

Overview

Using novel AI/ML techniques, ZeroIn zeroes-in onto problems in software repositories and aims to identify code vulnerabilities at their very origin, namely, at the time at which developers commit their codes into their repositories.

ZeroIn: Characterizing the Data Distributions of Commits in Software Repositories

A characterization of the software development metadata is presented in terms of distributions of data that best captures the trends in the datasets, to feed into the machine learning components of ZeroIn to exploit connectivity among the sets of repositories, commits, and developers.

Kalyan Perumalla, Aradhana Soni, Rupam Dey, Steven Rich

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Using Machine Learning Towards Early Flagging of Potentially Buggy Software Commits

Using multiple classifiers we verify the feasibility of using metadata from synthetic datasets modeled by a characterization of a few large software repositories and developer profiles. Results show that the metadata-based learning approach appears promising towards early flagging of potentially buggy commits in software repositories.

Aradhana Soni, Kalyan Perumalla

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Characterizing the Distributions of Commits in Large Source Code Repositories

We present preliminary results from characterizing the distribution of 452 million commits in a metadata listing from GitHub repositories. Based on multiple distributions, we find the best fits and second best fits across different ranges in the data. The characterization is aimed at synthetic repository generation suitable for use in simulation and machine learning.

Aradhana Soni, Kalyan Perumalla, Rupam Dey

PDF

Organization

Sponsor: Industry
Institutions: University of Tennessee, Knoxville
Period: 2021-2024

Gallery

Graph Networks

Fri, 01 Jul 2022 00:00:00 +0000

Parallel/Distributed implementation (1000s of GPUs), Graph algorithms, Graph Generation on multiple GPUs, Community detection-based solutions

Overview

See all items tagged Graph

Very large network generation (trillion-edge)
Very fast network generation
- GPU-based scalable algorithms
- Billion of edges per second
Scale-free and distribution-conformant networks
Community detection-based applications
- Electric grid distribution network inference

Fast GPU-Based Generation of Large Graph Networks From Degree Distributions

A novel algorithm has been designed, developed, and implemented on modern GPU accelerators, and benchmarked on networks with billions of edges, including Facebook and Twitter networks. Rates of generation exceed 50 billion edges per second.

Maksudul Alam, Kalyan Perumalla

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Scale-Free Graph Networks with Trillions of Edges: Rapid Generation using 1000 GPUs

Our algorithm cuPPA generates scale-free networks using the preferential-attachment model, custom-designed to exploit multiple GPUs. We generate extremely large scale-free networks of 4 trillion edges in less than 8 minutes using 1,008 NVIDIA Volta GPUs of the Summit supercomputer. This represents the first ever graph network generation at this scale of parallel execution with over thousand GPUs. Moreover, our algorithm is uniquely suitable for generating networks in a streaming mode without the need for explicitly storing (writing to disk) the entire network, and is suitable for targeting even larger scales with quadrillions of edges.

Maksudul Alam, Kalyan Perumalla

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On the Robustness of Network Community Structure Under Addition of Edges

We study the impact of edge additions on the community structure using Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks. We show that, for a fixed network size, the impact of edge additions is greater on networks with initially weak community structure than on networks with strongly clustered structures. Also, we find that the perception of the impact is also dependent on the community detection algorithm used to uncover communities.

Pablo Moriano, Kalyan Perumalla

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Novel Parallel Algorithms for Fast Multi-GPU-Based Generation of Massive Scale-Free Networks

A novel parallel algorithm, cuPPA, is presented for generating random scale-free networks using the preferential attachment model. The algorithm is custom-designed for ‘single instruction multiple data (SIMD)’ for GPUs. Our algorithm is the first to exploit GPUs, and also the fastest implementation available today, for scale-free networks. On an NVidia GeForce 1080 GPU, cuPPA generates a scale-free network of two billion edges in less than 3 s. On multi-GPU platforms, cuPPA-Hash generates a scale-free network of 16 billion edges in less than 7 s using a machine consisting of 4 NVidia Tesla P100 GPUs.

Maksudul Alam, Kalyan Perumalla, Peter Sanders

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GPU-based parallel algorithm for generating massive scale-free networks using the preferential attachment model

A novel parallel algorithm, cuPPA, is presented for generating random scale-free networks using the preferential-attachment model. In one of the best cases, when executed on an NVidia GeForce 1080 GPU, cuPPA generates a scale-free network of two billion edges in less than 3 seconds.

Maksudul Alam, Kalyan Perumalla

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Generating Billion-Edge Scale-Free Networks in Seconds: Performance Study of a Novel GPU-based Preferential Attachment Model

A novel parallel algorithm is presented for generating random scale-free networks using the preferential-attachment model. In one of the best cases, when executed on an NVidia GeForce 1080 GPU, cuPPA generates a scale free network of a billion edges in less than 2 seconds.

Kalyan Perumalla, Maksudul Alam

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GPU-based Real-Time Execution of Vehicular Mobility Models in Large-Scale Road Network Scenarios

A methodology and its associated algorithms are presented for mapping a novel, field-based vehicular…

Kalyan Perumalla, Brandon Aaby, Srikanth Yoginath, Sudip Seal

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Network Simulation

A detailed introduction to the design, implementation, and use of network simulation tools is presen…

Richard Fujimoto, Kalyan Perumalla, George Riley

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Large-Scale Network Simulation - How Big? How Fast?

[Pub 44]

Richard Fujimoto, Kalyan Perumalla, Alfred Park, Hao Wu, Mostafa Ammar, George Riley

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DES Grid

Sat, 01 Jan 2022 00:00:00 +0000

Discrete Event Modeling of the Electric Grid is a new non-equilibrium, transient analysis model and solver that is indispensable for new advancements in grids with high renewable penetration.

Novel Discrete Event Modeling and Simulation of Energy Grids

Overview

This project is aimed at creating and demonstrating a new approach to transient analysis for power systems that has three key advantages over existing methods. These advantages are:

an accurate, physically plausible model that creates signals perceived by sensors throughout the transmission and distribution networks;
enabling a natural and accurate representation of modern electrical loads, such as power electronics devices, within the distribution system, and
a computationally tractable method of simulating these models at large scales.

Gallery

Approach

Grid modeling and simulation requires accurate capture of physics combined with internal and external behavioral elements. The physics includes movement of electricity at multiple scales broadly classified as transmission, distribution, and sub-distribution. This is driven by internal behaviors: generation is dictated by physics of power generators plus sophisticated controls to maintain voltage, and by consumption loads driven by a wide variety of traditional energy sinks as well as increasingly sophisticated sensors and controllers introduced by smart grid technologies. In light of advances in distributed energy resources and renewables, most aspects of transport, generation, consumption and responses are also greatly controlled by rich internal behaviors. Our approach provides support in one form or another to modeling and simulating all the aforementioned grid elements.

We approach the grid modeling and simulation problem with a first principles-based, fully generalized, transient simulation view of the grid. Our starting point is that of a new three-phase model that is equally applicable at multiple levels, spanning transmission, distribution and sub-distribution.

Our three-phase model is designed to produce voltage and current wave forms at scales in time and geographical spaces sufficient to understand system-wide dynamics resulting from localized-yet-interconnected behaviors of:

signal processing algorithms and logic in inverters, protection devices, and other equipment that act on millisecond time scales;
electrical and mechanical dynamics of conventional and new solar, fossil fuel, nuclear, and other power generators that evolve over tens of seconds; and
detailed dynamics of loads that may contribute to large-scale distributed controls that counterbalance the volatility of distributed/renewable generation.

We create a new type of power system model that integrates dynamic load and generation models with a dynamic, rather than quasi-equilibrium, model of the transmission and distribution network. This new model enables relevant, simulation-based performance assessments of new sensors, such as next generation phasor measurement units and distribution-based frequency sensors, and provide an unprecedented, indispensable understanding of how sensor characteristics impact wide area control strategies.

The new model developed through this research replaces pseudo equilibrium models of transmissions and loads with dynamic elements that properly resolve the physics that transmit electrical power. With these new models it becomes possible in simulation to measure physically plausible voltage signals throughout a large power system during a transient, and to do so with complete knowledge of the events within the modeled power system that produced the observed signal. This new insight will propel advancing sensing and control technologies that would be infeasible to engineer with present, inadequate models and experimental test beds.

Organization

Sponsor: US Department of Energy (DOE)
- Office: Office of Electricity (OE)
- Program:
  - Grid Modernization Laboratory Consortium (GMLC)
  - Advanced Grid Modeling (AGM)
Prime: Oak Ridge National Laboratory (ORNL)

Additional Motivation

Current methods for electromechanical transient analysis of electrical power systems predate the significant, recent advances in sensing technology that allow measurements of frequency to be made through the transmission and distribution systems. Consequently, current methods of analysis make no attempt to accurately calculate the signals that these sensors are designed to monitor. This creates considerable difficulties when current methods, models, and simulation tools are used to assess how a new device that relies on measures of frequency will affect the response of the power system to a disturbance,

This problem has been widely recognized in recent years, and numerous, essentially ad hoc, solutions have been proposed. While these ad hoc techniques can, and have, been easily integrated into existing tools, they generally lack a fundamental basis in the physics of an electrical power system, are prone to large numerical errors, or both. At best, these proposals must be viewed as a stop gap measure until models become available that properly resolve dynamical effects in the transmission network that, in conjunction with the generator dynamics, ultimately determine how a signal is perceived by a sensor within the transmission and distribution systems.

Selected Publications

A Case Study in Simulation Methods for Power Electronic Circuits

A simplified circuit is used as a case study to uncover and highlight key considerations in the use of traditional numerical simulation methods and compare them with those obtained from alternative methods that are discrete event-based from the outset. Results show the regimes where the traditional numerical methods and the alternative discrete event methods are applicable, and the need for discrete event approaches that precisely and efficiently resolve switching dynamics produced by power electronics systems that are important in emerging grid scenarios, such as large scale renewable energy.

James Nutaro, Suman Debnath, Kalyan Perumalla

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AI ML Research

Wed, 01 Dec 2021 00:00:00 +0000

My research interests in AI/ML have focused on applying the technologies and software to applications in cyber-physical systems, digital twins, cybersecurity verification, and intelligent mechanical design. My research has largely used existing AI/ML methods and using them effectively in novel ways to solve difficult problems in various domains. Nevertheless, I am interested in the fundamental concepts underlying the methods and developing computational implementations from scratch, as, for example, in new Recurrent Neural Network implementations from scratch, not entirely relying on popular AI/ML packages.

From the computational point of view, my other major interests are in the end-to-end scalability of AI/ML solutions, especially in relation to novel hardware architectures, down-to-the-metal considerations, hardware-software interplay and codesign, and holistic scalability of workflows. Of particular interest are new AI/ML problems that are characterized by volumes and velocities massively higher than what typical desktop-based and cluster-based solutions address.

While reaping the benefits of well known and tested methods, my inclination is to keep an eye towards basic insights, unique combinations, and new techniques.

Overview

Projects

Deep CYBERIA
- CNN: Sensor detection
- RNN: Sensor time-series
CYVET
- NLP: CyBERT over BERT
- NLP: Claim detection
- CNN: Document classification
- CNN: Product detection
Digital Twin Framework
- RNN: Anomaly detection
DeepEx
- CNN: Material image classification
- VAE: Molecular Dynamics
Intelligent Design
- RL: Steering
Energy Management
- RL: Dynamic learning and adaptation
Kensor
- Critical Facility Monitoring
- Novel Markov modeling

Software

Python
Jupyter
numpy/no-numpy
CUDA
CUDNN
MPI
Tensorflow (2/Keras)
PyTorch

Organization

Sponsors: Various, across multiple projects
- US Department of Energy (DOE)
- US Department of Defense (DoD)
- Oak Ridge National Laboratory-Directed Research and Development (LDRD)
- Industry

Publications

Listed under the AI/ML category under Publications At-a-glance

Biography

Mon, 01 Jan 0001 00:00:00 +0000

Kalyan Perumalla is a computer scientist focused on research in supercomputing, quantum computing, and artificial intelligence, as research staff member, faculty, and program manager with the U.S. government, national labs, and universities.

He served as a Federal Program Manager in Advanced Scientific Computing Research at the U.S. Dept. of Energy, Office of Science, where he managed a $100-million R&D portfolio covering Artificial Intelligence (AI), High-Performance Computing (HPC), Quantum Computing and Networking, Scientific Discovery through Advanced Computing (SciDAC), Basic Computer Science, and Small Business Innovation Research (SBIR) Programs.

At DOE/ASCR, he contributed to the ideation, formulation, issuance, and management of 12 large solicitations.

Some of his major assignments included oversight of the following:

Quantum Science Center (QSC), one of the five national quantum information science research centers (NQISRCs)
Advanced Quantum Testbed (AQT)
Quantum Scientific Computing Open User Testbed (QSCOUT)
SciDAC Computer Science Institute (RAPIDS2)
Computer Science segment of the Base Program (Competitive Portfolios)
Artificial Intelligence (AI) for Science (Scientific Software); and
Several Phase I and Phase II projects under the Small Business Innovation Research Program in Artificial Intelligence, Digital Twins, Quantum Computing and Networking, and Combinatorial Optimization.

Additional information can be found at Funding Opportunities, Solicitations, and PI list.

Before joining DOE, he led advanced R&D as Distinguished Research Staff Member at the Oak Ridge National Laboratory (ORNL) developing scalable software and applications on the world’s largest supercomputers for 17 years, including as a line manager and a founding group leader, where he made contributions progressing up to the Exascale Computing Project (ECP).

He is among the first cohort of recipients of the U.S. Department of Energy Early Career Award in Advanced Scientific Computing Research ($2.5 million for research over 5 years).

Over the past 25 years, he has served as a principal investigator (PI) or co-PI on several research projects sponsored by agencies including the Department of Energy (DOE), Department of Homeland Security (DHS), Air Force, DARPA, Army Research Laboratory (ARL), National Science Foundation (NSF), and industry.

His major areas of technical contributions include scalable parallel/distributed software systems, high-performance computing, GPU accelerated computing, parallel discrete event simulation, reversible computing, cyber-physical systems, and applications of machine learning.

He is the author of “Introduction to Reversible Computing,” a seminal book in the fundamental theory and analysis of energy in computation. He co-authored another book, three book chapters, and about 150 articles in conferences and journals. He has delivered several advanced tutorials and lectures in defense simulation technologies, parallel systems, and reversible computing. Five (5) of his co-authored papers received the best paper awards, in 1999, 2002, 2005, 2008, and 2014.

Dr. Perumalla served on international conference program committees and editorial boards of journals. He also held leadership roles as program chair or co-chair of multiple international conferences across areas such as parallel simulation to cybersecurity.

Some of his research software in parallel and distributed computing have been disseminated to research institutions worldwide. His algorithms and software prototypes have been scaled to over 200,000 processor cores and 1000s of GPUs on large supercomputing systems, including the Oak Ridge Leadership Computing Facility’s Jaguar, Titan, and Summit series of supercomputers.

He also served on the Special Interest Group Governing Board of the Association for Computing Machinery (ACM) as the elected chair for ACM Special Interest Group in Simulation (SIGSIM).

He held full-time research faculty appointments 1997-2005 at the Georgia Institute of Technology. He also served as Fellow of the Institute of Advanced Study at Durham University, UK, and as member of the National Academies’ Technical Advisory Boards for the U.S. Army Research Laboratory.

He has earned certifications in Agile Development technologies as a Certified ScrumMaster, Certified Scrum Product Owner, Certified Scrum-at-Scale Practitioner, and Certified SAFe Scaled Agile Practitioner.

He previously held a Q (Top Secret) Security Clearance.

Digital Twin Framework

Sat, 01 Jan 2022 00:00:00 +0000

Our novel Digital Twin Framework (DTF) is designed to improve resilience of critical infrastructure systems by continuously comparing the infrastructure state with automatically generated, AI/ML-based, real-time digital-twin simulation of the system.

Team

Digital Twin Framework Software Architecture

Overview

As the level of automation in critical infrastructure increases, the ability to detect cyber intrusions becomes more crucial and extremely challenging. Recent cyber attacks demonstrate the devastating and widespread affects they can have on critical infrastructure.

DTF is designed specifically to detect and eventually prevent such attacks, with models validated against experimental data from two critical infrastructure experimental emulators – a canal lock system and an electric distribution system – exhibiting very different dynamics. The canal lock system’s digital twin uses a recurrent neural network trained from the experimental data collected via the DTF. A digital twin of the transmission system is created using a commercial real-time power systems simulator and integrated into our DTF along with the hardware, embedded controllers, and live sensor data using the Open Field Message Bus data model, and publish/subscribe communication protocols. A cyber attack is used on both systems to demonstrate the DTF’s detection capability.

Organization

Sponsor: Lab Directed Research and Development, Oak Ridge National Laboratory
Period: 2018-2020

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On the Effectiveness of Recurrent Neural Networks for Live Modeling of Cyber-Physical Systems

We empirically study the effectiveness of Recurrent Neural Network (RNN)-based models as the basis of DT-based resilience and uncover the important characteristics of an RNN-based solution with experimentation on a lab-scale Canal Lock CPS emulator with live validations and attack scenarios. For the first time, we demonstrate actual, real-time use of a RNN-based model as a DT for performing live analysis on an operational CPS.

Srikanth Yoginath, Varisara Tansakul, Supriya Chinthavali, Curtis Taylor, Joshua Hambrick, Philip Irminger, Kalyan Perumalla

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A Digital Twin Framework for Testing, Evaluation and Deployment of Resilient Cyber-physical Systems

We describe an approach to detecting and preventing cyber attacks by continuously comparing the infrastructure state with a real-time digital-twin simulation of it. Specifically, we describe and demonstrate a Digital Twin Framework (DTF) designed specifically to detect and eventually prevent such attacks. The canal lock system’s digital twin uses a recurrent neural network trained from the experimental data collected via the DTF.

Raymond Hink, Mark Buckner, Supriya Chinthavali, Chris Craig, Timothy Daniel, Joel Dawson, Milton Ericson, Joshua Hambrick, Philip Irminger, Ryan Kerekes, Juan Lopez, Kalyan Perumalla, Nicholas Peters, Stacy Prowell, Varisara Tansakul, Curtis Taylor, Bailu Xiao, Srikanth Yoginath

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EpiClone: Epidemiological Clonable Simulations

Sat, 01 Jan 2022 00:00:00 +0000

Cloned simulations of epidemiological outbreaks like COVID-19 can now rapidly evaluate millions of what-if scenarios at national and world scales, and efficiently exploit 1000s of GPUs.

EpiClone model elements

EpiClone simulating USA scenarios

Overview

EpiClone represents the next advancements in what-if decision evaluation on state-of-the-art accelerated computing platforms including supercomputers containing thousands of GPUs.

EpiClone addresses global epidemiological outbreaks via an incremental simulation-based what-if decision tree evolution. EpiClone offers new scaling capabilities that were previously not possible before for rapidly simulating thousands or millions of epidemiological scenarios.

The EpiClone approach and results in scalable modeling and fast scenario exploration provide a leap in the analyses of epidemics and other complex systems.

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Organization

Sponsor: Lab Directed Research and Development, Oak Ridge National Laboratory
Period: 2015-2017

Scalable Cloning on Large-Scale GPU Platforms with Application to Time-Stepped Simulations on Grids

Cloning is a technique to efficiently simulate a tree of multiple what-if scenarios that are unraveled during the course of a base simulation. We present the conceptual simulation framework, algorithmic foundations, and runtime interface of CloneX, a new system we designed for scalable simulation cloning.

Srikanth Yoginath, Kalyan Perumalla

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Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs: Fast What–If Analyses of Localized and Dynamic Effects

A mesoscopic modeling approach is described that strikes a middle ground between macroscopic models based on coupled differential equations and microscopic models built on fine-grained behaviors at the individual entity level. Execution of our implementation scaled to 8192 GPUs of supercomputing platforms demonstrates the ability to rapidly evaluate what–if scenarios several orders of magnitude faster than the conventional methods.

Kalyan Perumalla, Maksudul Alam

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Discrete Event Modeling and Massively Parallel Execution of Epidemic Outbreak Phenomena

In complex phenomena such as epidemiological outbreaks, the intensity of inherent feedback effects a…

Kalyan Perumalla, Sudip Seal

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Reversible Parallel Discrete-Event Execution of Large-scale Epidemic Outbreak Models

The spatial scale, runtime speed, and behavioral detail of epidemic outbreak simulations altogether …

Kalyan Perumalla, Sudip Seal

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Network Simulation

Sat, 01 Jan 2022 00:00:00 +0000

ISBN 978-1439873403 | Morgan & Claypool | Amazon
This book covers all concepts in modeling and simulating wired and wireless computer networks using parallel computing to reach Internet-scale simulations.

Overview

Network Simulation

A detailed introduction to the design, implementation, and use of network simulation tools is presented. The requirements and issues faced in the design of simulators for wired and wireless networks are discussed. Abstractions such as packet- and fluid-level network models are covered. Several existing simulations are given as examples, with details and rationales regarding design decisions presented. Issues regarding performance and scalability are discussed in detail, describing how one can utilize distributed simulation methods to increase the scale and performance of a simulation environment. Finally, a case study of two simulation tools is presented that have been developed using distributed simulation techniques. This text is essential to any student, researcher, or network architect desiring a detailed understanding of how network simulation tools are designed, implemented, and used.

Reference

Network Simulation

A detailed introduction to the design, implementation, and use of network simulation tools is presen…

Richard Fujimoto, Kalyan Perumalla, George Riley

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Intelligent Design of Structure for Function

Sat, 01 Jan 2022 00:00:00 +0000

Intelligent Design is a novel paradigm for automated, generalized, and optimized physical structural design of 3D geometry meeting a desired function.

Intelligent Design

Overview

In engineering design, an engineer starts with an initial heuristic structure and then iteratively modifies it until it satisfies all the design requirements. This lacks a thorough theoretical basis to account for the vast design space for a given problem. All current design methods are limited or biased by historical knowledge. What if this traditional design paradigm were dropped and an entirely new, unbiased, thorough framework were adopted?

Our novel Intelligent Design is the first approach to mix additive as well as subtractive steps in intelligently exploring the design space. Furthermore, it is fundamentally delinked from the traditional necessity to start with a closely related design that has been previously proven to be suitable.

(a) Traditional approach with structural determination methods that start with (b) Solid block, and (c) Minimal connection designs

Organization

Sponsor: Laboratory-Directed Research and Development, Oak Ridge National Laboratory

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CloneX

Sat, 01 Jan 2022 00:00:00 +0000

Cloned eXecution of simulations is a powerful decision-making system to incrementally evaluate millions of what-if scenarios and scale to 1000s of GPUs.

Illustration of CloneX what-if tree on diffusion processes

CloneX computational and memory savings with increasing tree depth

Overview

CloneX is a novel system we designed for scalable simulation cloning, consisting of a conceptual simulation framework, algorithmic foundations, and a highly scalable runtime interface. It efficiently and dynamically creates whole logical copies of a dynamic tree of simulations across a large parallel system without full physical duplication of computation and memory.

CloneX Software Architecture

Titan Supercomputer with 1000s of GPUs

CloneX efficiently simulates a tree of multiple what-if scenarios unraveled during the course of a normal (base) simulation. Cloned execution is highly challenging to realize on large, distributed memory computing platforms, due to the dynamic nature of the computational load across clones, and due to the complex dependencies spanning the clone tree.

CloneX has been tested on 1000s of GPUs of a supercomputing system and evaluated with multiple benchmarks – such as heat diffusion, forest fire, and disease propagation models – delivering a speed up of over two orders of magnitude compared to replicated runs.

CloneX represents a major leap in ensemble simulations as a significantly faster and scalable way to execute many what-if scenarios of large simulations.

Organization

Sponsors: US Department of Energy (DOE)
- Office: Advanced Scientific Computing Research (ASCR)
  - Program: Early Career Research Program (ECRP)
- Office: ORNL Strategic Planning Office
  - Program: Laboratory-Directed Research and Develoopment (LDRD)
Period: 2015-2017

Gallery

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs: Fast What–If Analyses of Localized and Dynamic Effects

Kalyan Perumalla, Maksudul Alam

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Scalable Cloning on Large-Scale GPU Platforms with Application to Time-Stepped Simulations on Grids

Srikanth Yoginath, Kalyan Perumalla

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Kensor: Coordinated Intelligence from Co-located Sensors

Sat, 01 Jan 2022 00:00:00 +0000

Kensor uncovers coordinated intelligence about normal and abnormal phenomena from multiple sensors co-located in close proximity in secure installations.

Kensor

Overview

Given a set of co-located sensors, we seek an intelligent approach that would automatically determine the “normal” patterns of behaviors among the correlated sensors.

After normal behavior is extracted, later monitoring should detect any deviant variations over time.

An example application is an entry monitoring and alert system for facilities such as nuclear reactors, where badge readers, door locks, lights, weight trackers and other co-located sensors at the entry point are collectively tracked.

Gallery

Organization

Sponsor: Nuclear Safety
Period: 2019-2020

Kensor: Coordinated Intelligence from Co-Located Sensors

Here, we focus on coordinated intelligence about normal and abnormal phenomena from multiple sensors geographically co-located, monitoring and controlling a set of co-located devices. Given a set of co-located sensors, we develop an intelligent approach that automatically determines the ’normal’ patterns of behaviors among the correlated sensors. After normal behavior is extracted, later monitoring detects deviant variations over time.

Olivera Kotevska, Kalyan Perumalla, Juan Lopez

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HELICS

Sat, 01 Jan 2022 00:00:00 +0000

Hierarchical Engine for Large-Scale Infrastructure Co-Simulation (HELICS) is the first open-source software platform that co-simulates the nation’s power grid across transmission, distribution, and data communication systems.

Hierarchical Engine for Large-Scale Infrastructure Co-Simulation (HELICS)

Overview

HELICS enables grid planners and operators to understand how to navigate challenges such as integrating large-scale renewable energy resources and digital devices and defending against natural disasters and cyber threats.

HELICS was nominated to the R&D100 awards and was selected as an R&D100 Award finalist in 2018.

R&D100 Finalist Award for HELICS

Organization

Sponsor: US Department of Energy (DOE)
- Office: Office of Electricity (OE)
- Program: Grid Modernization Laboratory Consortium (GMLC)
Period: 2015-2017
Team: ORNL, PNNL, LLNL, ANL, and others

BLOCKTRI: Parallel Block Tridiagonal Solver

Sat, 01 Jan 2022 00:00:00 +0000

Our Block Tridiagonal Solver is one of the fastest parallel solvers for scientific codes, written in FORTRAN and MPI using the block cyclic algorithm, and tested with plasma equilibrium simulations for fusion energy tokamaks and astrophysics applications.

BLOCKTRI

Organization

Sponsor: US Department of Energy (DOE)
- Office: Office of Science (SC)
- Program: Fusion Energy and International Thermonuclear Experimental Reactor (ITER)
Prime: Oak Ridge National Laboratory (ORNL)

Selected Publications

Bcyclic: A Parallel Block Tri-diagonal Matrix Cyclic Solver

A block tri-diagonal matrix is factored with minimal fill-in using a cyclic reduction algorithm that…

Steven Hirshman, Kalyan Perumalla, Vickie Lynch, Raul Sanchez

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Revisiting Cyclic Reduction and Parallel Prefix-Based Algorithms for Tri-diagonal Systems of Equations

Direct solvers based on prefix computation and cyclic reduction algorithms exploit the special struc…

Sudip Seal, Kalyan Perumalla, Steven Hirshman

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Scaling the SIESTA Magnetohydrodynamics Equilibrium Code

We report the results of a scaling effort that increases both the speed and resolution of the SIESTA…

Sudip Seal, Kalyan Perumalla, Steven Hirshman

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Improved Parallelization of the SIESTA Magneto-hydrodynamic Equilibrium Code Using Cyclic Reduction

SIESTA is a parallel three-dimensional plasma equilibrium code capable of resolving magnetic islands…

Sudip Seal, Steven Hirshman, Kalyan Perumalla

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Efficient Heterogeneous Execution on Large Multicore and Accelerator Platforms: Case Study Using a Block Tridiagonal Solver

The algorithmic and implementation principles are explored in gainfully exploiting GPU accelerators in conjunction with multicore processors on high-end systems…

Alfred Park, Kalyan Perumalla

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NetWarp

Sat, 01 Jan 2022 00:00:00 +0000

NetWarp is a novel time-synchronized virtual machine(VM)-based parallel simulation framework that accurately lifts the devices and the network communications to a virtual time plane while retaining full fidelity.

NetWarp

Organization

Sponsors: US Department of Defense
- Office: Army Research Laboratory (ARL)
- Office: Missile Defense Agency (MDA)
- Programs: Computational Sciences, STTR
Prime: Oak Ridge National Laboratory (ORNL)

Gallery

summary: Secure Virtual Environment for Cyber Resiliency Validation building on NetWarp technology for cybersecurity in hardware and software of missile defense systems

Virtual Machine-Based Simulation Platform For MANET-Based Cyber Infrastructure

In modeling and simulating complex systems such as mobile ad-hoc networks (MANETs) in defense communications, …

Srikanth Yoginath, Kalyan Perumalla, Brian Henz

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Taming Wild Horses: The Need for Virtual Time-based Scheduling of VMs in Network Simulations

The next generation of scalable network simulators employ virtual machines (VMs) to act as high-fidelity models of traffic producer/consumer nodes…

Srikanth Yoginath, Kalyan Perumalla, Brian Henz

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Optimized Hypervisor Scheduler for Parallel Discrete Event Simulations on Virtual Machine Platforms

With the advent of virtual machine (VM)-based platforms for parallel computing, it is now possible to execute parallel discrete event simulations (PDES)…

Srikanth Yoginath, Kalyan Perumalla

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IAS Fellowship

Sat, 01 Jan 2022 00:00:00 +0000

Duham Unversity Institute of Advanced Study Fellowship gathers together the world’s finest scholars and non-academics (such as intellectuals, artists, writers, journalists, policy makers, and politicians) from the full spectrum of science, social science, arts and humanities disciplines to address themes of global significance.

Overview

The Institute of Advanced Study (IAS) provides its fellows with a setting that offers them freedom to think in an unconstrained way, exempt from the day to day demands of their normal professional obligations, and in the company of other thinkers from very different backgrounds. The IAS seeks to develop a truly global perspective by ensuring that its fellows are recruited from all over the world, including the global south. The IAS instigated a range of activities focused on advancing reflexive understandings of interdisciplinarity.

The IAS fellowship provided Dr. Perumalla with the opportunity to develop own research and ideas in a thriving community of intellectuals of national and international standing, within the Institute, through the College system, and by forging strong links with at least one department at Durham.

For the duration of his stay, Dr. Perumalla was provided with a single occupancy office in Cosin’s Hall, the home of the Institute of Advanced Study. The Hall, a magnificent listed building dedicated to the IAS and elegantly refurbished, is situated on a World Heritage site that includes Durham Cathedral and Durham Castle. In addition, all fellows were welcomed into a college community where they were offered half board accommodation (in a one bedroom flat), and membership of the college’s Senior Common Room. The IAS covered the costs associated with travel to Durham, UK from ORNL and provided an honorarium.

All Fellows

Dr. Perumalla’s Cohort

Organization

Sponsor: Duham University and Oak Ridge National Laboratory

Gallery

Normalcy, Magic, Miracle and Error: Emergence along a Reversibility Spectrum

Kalyan Perumalla

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On the Definability of Art

There are many key concepts that, even while being part of everyday life, elude definition. One such is “Art.” Here, possible ways are identified to define Art, along with a description of a few factors that underlie the challenge in arriving at a definition. Additionally, a candidate definition from a scientist’s viewpoint is proposed for an abstract, encompassing model.

Kalyan Perumalla, Richard Read

PDF

Rocks3D-HPC

Sat, 01 Jan 2022 00:00:00 +0000

Rocks3D-HPC is an efficient parallelization of the Rocks3D high-fidelity earth moving equipment simulation based on the Discrete Element Method implemented using FORTRAN, OpenMP multi-threading, and message passing interface (MPI) for high performance computing.

Organization

Sponsor: Caterpillar, Inc.
Institutions: ORNL

Gallery

Principal Investigators

Wed, 26 Feb 2025 00:00:00 +0000

Selected list of areas and Principal Investigators (PIs) or Co-PIs of R&D projects I managed as part of my diverse program management portfolio (2023-2025).

The R&D activity in my portfolio manifests as multi-institutional, multi-year projects led by principal investigators who are researchers from national laboratories, university faculty, and industry entrepreneurs.

PI/Co-PI	Institution	Area
AI-for-Science Program
Harshitha Menon	Lawrence Livermore National Laboratory	AI RA2 - Software
Keita Teranishi	Oak Ridge National Laboratory	AI RA2 - Software
Computer Science Base Program
Tammie Borders	National Energy Technology Laboratory	Competitive Portfolios - Computer Science
Dirk Van Essendelft	National Energy Technology Laboratory	Competitive Portfolios - Computer Science
Michael James	Cerebras Systems, Inc.	Competitive Portfolios - Computer Science
John Shalf	Lawrence Berkeley National Laboratory	Competitive Portfolios - Computer Science
Keita Teranishi	Oak Ridge National Laboratory	Competitive Portfolios - Computer Science
Franz Franchetti	Carnegie Mellon University	Competitive Portfolios - Computer Science
SciDAC Program
Lenny Oliker	Lawrence Berkeley National Laboratory	SciDAC - Computer Science Institute (RAPIDS2)
Rob Ross	Argonne National Laboratory	SciDAC - Computer Science Institute (RAPIDS2)
Quantum Computing Program
Travis Humble	Oak Ridge National Laboratory	Quantum Science Center (NQISRC)
Irfan Siddiqi	University of California, Berkeley	Advanced Quantum Testbed (AQT)
Susan Clark	Sandia National Laboratory	Quantum Scientific Computing Open Userer Testbed (QSCOUT)
Quantum Networking Program
Yanne Chembo	University of Maryland	Quantum Networking
Eden Figueroa	Brookhaven National Laboratory	Quantum Networking
Rajkumar Kettimuthu	Argonne National Laboratory	Quantum Networking
Joeseph Lukens	Purdue University	Quantum Networking
Inder Monga	Lawrence Berkeley National Laboratory	Quantum Networking
Cristian Pena	Fermi National Accelerator Laboratory	Quantum Networking
Nick Peters	Oak Ridge National Laboratory	Quantum Networking
Nageswara Rao	Oak Ridge National Laboratory	Quantum Networking
Mariam Kiran	Oak Ridge National Laboratory	Quantum Networking
Panagiotis Spentzouris	Fermi National Accelerator Laboratory	Quantum Networking
Don Towsley	University of Massachusetts	Quantum Networking
EXPRESS Program
Kevin Brown	Argonne National Laboratory	EXPRESS - Space Time Memory
Emily Dolson	Michigan State University	EXPRESS - Space Time Memory
Jason Liu	Flordia International University	EXPRESS - Space Time Memory
Nandakishore Shanti	Los Alamos National Laboratory	EXPRESS - Space Time Memory
Luis Zaman	University of Michigan	EXPRESS - Space Time Memory
SBIR/STTR Program
Vijay Hanagandi	OSI Opt	SBIR - Mixed Integer Programming
Caitlin Ross	Kitware, Inc	SBIR - Digital Twins
Pranav Gokhale	Infleqtion, Inc	SBIR - Quantum Computing
Keiko Munechika	HighRI Optics, Inc	SBIR - Quantum Networking
Sowmya Ramachandran	Stottler Henke Assoc. Inc	SBIR - Artificial Intelligence/LLM
Svitlana Volkova	Aptima, Inc	SBIR - Artificial Intelligence/LLM

MutEnt

Fri, 15 Apr 2022 00:00:00 +0000

Mutual Entropy-based Image Registration

Overview

MutEnt

Organization

Sponsor: Department of Defense

Computing a Non-trivial Lower Bound on the Joint Entropy between Two Images

In this report, a non-trivial lower bound on the joint entropy of two non-identical images is developed, which is greater than the …

Kalyan Perumalla

PDF Cite DOI

Computational Speed and Matching Quality using an Upper Bound on the Normalized Mutual Information

URL

Kalyan Perumalla, Maksudul Alam, Devin A White

Cite

Gallery

Advisors

Sat, 01 Jan 2022 00:00:00 +0000

Salutations to my kind preceptors from whom I learned the ABC’s of research, tenacity, hardwork, and integrity

Prof. Richard Fujimoto

Aug 1993 - Dec 1999
Georgia Institute of Technology
Now: Regents’ Professor
Georgia Institute of Technology

Doctoral Thesis Advisor
Parallel Discrete Event Simulation, High Level Architecture, Network Simulation, Reversible Computing, High Performance Computing, and more
Co-developed artifacts

Prof. Udaya Vemulapati

Aug 1991 - Jun 1993
University of Central Florida
Now: Founder
uDASH Information Technologies

Master’s Thesis Advisor
Operating Systems, Distributed Systems, Computational Linear Algebra, and more
Co-developed artifacts

Visualizing the National Energy Grid

Sat, 01 Jan 2022 00:00:00 +0000

EGIVAC, System for Electric Grid Interactive Visualization, Animation, and Control, is one of the ground-breaking, feature-rich, large-scale visualizations of the US national energy grid.

Visualization on the wall

Overview

In 2006-07, developed the first grid visualization system from the ground up, scaling to the full national grid sizes, 100KV to 700KV+.

The system opened up, for the first time ever, the ability to dynamically customize geophysical layers and icons together with grid network buses and lines, shown with accurate latitude and longitude coordinates synchronized with rich geographical layers.

Gallery

Interactive, visual differentiation based on voltage ratings was achieved for the first time at the national scale incorporating large interconnects including ERCOT and Eastern Interconnect.

The capability for the rendering to scale even from smaller personal desktop displays up to the largest, wall-sized multi-monitor systems was achieved for the first time for national energy grids.

Dynamic, interactive animation capabilities provided a natural, visual evaluation of contingencies to decisionmaking teams, with the potential to tie real-time weather data streams with grid state.

Impact

This visualization system went onto inspire large investments by the US Department of Energy and other agencies in grid monitoring centers and the development of next generation systems such as VERDE and EAGLE-I.

My work also featured prominently in the research highlights of the Computational Sciences and Engineering Division across multiple years, and won the laboratory director’s award among all the lab-directed research and development projects in 2007.

Organization

Sponsor: US Department of Energy (DOE)
- Office: Office of Electricity (OE)
Prime: Oak Ridge National Laboratory (ORNL)

Features

EGIVAC is a graphical user interface designed to help electric grid operators to visualize, animate and control the operation of the grid in a highly interactive fashion. The system has been designed with the objectives of interactivity of display/control and of reflecting geographical correspondence of electrical elements. The tool is also intended to serve as an intelligent front-end to control, such as for rapid contingency analysis using parallel/distributed execution of multiple future scenarios.

Visualization

Rendering tuned to minimize clutter at nation-scale display
Color coded buses/lines for high contrast in multitude
Large screen display on Power Wall
Interactive display filters based on voltages (KV)
Population/customer coverage & reach (work in progress)
Hands-off panning
Zoom in/out

Animation

Time-driven animation
Pause/Resume
Interactive slow/fast speed control
Phasor Measurement Units (PMU) animation (planned)
Morphing geographical topology into electrical circuit (series/parallel) (work in progress)
Dynamic graphs of affected operational measures (work in progress)

Control

Visual alert effects (blinking, color changes)
Event impact analysis (e.g., population affected by hurricanes)
Parallel targeted contingency analysis (work in progress)

Resolution

Tested with 45,000 buses and 60,000 lines
Smooth animation (1 to 30 frames per second)

Platform

Java (portable to Mac, Windows, Linux, and other platforms)

Fact Sheet

Postdoctoral Advisees

Sat, 01 Jan 2022 00:00:00 +0000

Pedigree that makes me proud in the lineage of knowledge generation and dissemination

Dr. Sudip Seal

Nov 2007 - Nov 2009
Oak Ridge National Laboratory
Now: Senior R&D Staff Member
Oak Ridge National Laboratory

Parallel Algorithms, High Performance Computing
Co-developed artifacts

Dr. Alfred Park

Nov 2010 - Nov 2012
Oak Ridge National Laboratory
Now: Software Engineer
Microsoft, Inc.

Parallel Discrete Event Simulation, Reversible Computing
Co-developed artifacts

Dr. Maksudul Alam

Dec 2016 - Jun 2019
Oak Ridge National Laboratory
Now: Research Staff Member
Oak Ridge National Laboratory

HPC, GPU-based Algorithms, Simulation, Graph Generation
Co-developed artifacts

Dr. Olivera Kotevska

Feb 2019 - Mar 2020
Oak Ridge National Laboratory
Now: Research Staff Member
Oak Ridge National Laboratory

Internet of Things, Cyber-Physical Systems
Co-developed artifacts

Co-advised Students

Sat, 01 Jan 2022 00:00:00 +0000

Students I co-advise(d) as committee member in their doctoral or master’s program

Kimia Ameri

Sep 2019 - Present
University of Nebraska-Lincoln
Now: Doctoral Student | Graduate Research Assistant
University of Nebraska-Lincoln (UNL), Omaha, Nebraska, USA

Advisor: Prof. Hamid Sharif, UNL
Co-developed artifacts

Dr. Philipp Andelfinger

Feb 2014 - Feb 2016
Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Now: Postdoctoral Researcher
University of Rostock

PhD Thesis
“Identifying and Harnessing Concurrency for Parallel and Distributed Network Simulation” Advisor: Prof. Hannes Hartenstein, KIT
Co-developed artifacts

Dr. Bilel Romdhanne

Jan 2010 - Dec 2013
EURECOM/Telecom ParisTech, Nice, France
Now: Research Scientist
Amadeus Labs, Valbonne, Provence-Alpes-Côte d’Azur, France

PhD Thesis
“Simulation des Réseaux à grande Échelle sur les architectures de calcules hètèrogénes” Advisor: Prof. Navid Nikaein, Telecom ParisTech
Co-developed artifacts

Doctoral Students

Sat, 01 Jan 2022 00:00:00 +0000

Persevering and creative minds whom I train in deep thought to reach the peak of their formal education

Dr. Srikanth Yoginath

Aug 2007 - Aug 2009
Georgia Institute of Technology
Now: R&D Staff Member
Oak Ridge National Laboratory

Graduation and Thesis
“Virtual Time-Aware Virtual Machine Systems”
Co-developed artifacts

Aradhana Soni

May 2021 - Jan 2024
University of Tennessee, Knoxville
Now: Graduate Research Assistant
Department of Industrial and Systems Engineering

Data Science, Graph-based Methods
Co-developed artifacts

Rupam Dey

May 2021 - May 2024
University of Tennessee, Knoxville
Now: Graduate Research Assistant
Department of Industrial and Systems Engineering

Data Science, Synthetic Data Generation
Co-developed artifacts

RealSim: Real-Time Simulations

Sat, 01 Jan 2022 00:00:00 +0000

Real-time Simulations is a simulation framework for fast evaluation of national emergency scenarios such as hurricanes, evaluated with large data streams and real-time computation on the latest hardware platforms on the edge.

RealSim

Gallery

Organization

Sponsor: Department of Homeland Security (DHS)
Period: 2006-2009

Towards Highly Interactive, GPU-based Evaluation of Evacuation Transport Scenarios at State-Scale

In large-scale scenarios, transportation modeling and simulation is severely constrained by simulati…

Kalyan Perumalla, Brandon Aaby, Srikanth Yoginath, Sudip Seal

Cite

Towards Highly Interactive, GPU-based Evaluation of Evacuation Transport Scenarios at State-Scale

In large-scale scenarios, transportation modeling and simulation is severely constrained by simulati…

Kalyan Perumalla, Brandon Aaby, Srikanth Yoginath, Sudip Seal

PDF Cite Slides

An Analysis Approach to Large-Scale Vehicular Network Simulations

[Pub 28]

Kalyan Perumalla, Martin Beckerman

PDF Cite

On Accounting for the Interplay of Kinetic and Non-Kinetic Aspects of Population Mobility Models

[Pub 31]

Kalyan Perumalla

PDF Cite

Efficient Simulation of Agent-Based Models on Multi-GPU and Multi-Core Clusters

An effective latency-hiding mechanism is presented in the parallelization of agent-based model simul…

Brandon Aaby, Kalyan Perumalla, Sudip Seal

PDF Cite Slides

Computational Spectrum of Agent Model Simulation

The study of human social behavioral systems is finding renewed interest in military, homeland secur…

Kalyan Perumalla

Cite

A Connectionist Modeling Approach to Rapid Analysis of Emergent Social Cognition Properties in Large-Populations

Traditional modeling methodologies, such as those based on rule-based agent modeling, are exhibiting…

Kalyan Perumalla, Jack C. Schryver

PDF Cite Slides

Data Parallel Execution Challenges and Runtime Performance of Agent Simulations on GPUs

[Pub 27]

Kalyan Perumalla, Brandon Aaby

PDF Cite

A Case Study of Efficient Social Network Simulation through General Processing on Graphics Processing Units (GPGPUs)

Agent based simulation has been both a large area of study and a widely used tool for scientific research in past years…

Brandon Aaby, Kalyan Perumalla

Cite

Sabbatical Guests

Wed, 01 Dec 2021 00:00:00 +0000

Faculty members I had the pleasure of serving as the host for their sabbaticals and performing joint research with them

Prof. Kamesh Madduri

May 2017 - Aug 2018
Pennsylvania State University
Now: Associate Professor

Combinatorial Optimization, Graph Algorithms

Prof. Viveck Cadambe

Jun 2018 - Aug 2019
Pennsylvania State University
Now: Associate Professor

Information Theory, High Performance Computing

Graduate Interns

Sat, 01 Jan 2022 00:00:00 +0000

Highly motivated, disciplined and hardworking graduate students with whom I had the pleasure of learning advanced technologies together

Aakriti Upadhyay

Jun 2019 - Aug 2019
State University of New York, Albany
Now: PhD student
University at Albany, SUNY

Efficient Sparse Matrix Computations on Graphical Processing Units

James Fox

Jun 2018 - Aug 2018
Georgia Institute of Technology
Now: PhD Student
Georgia Institute of Technology

Community Detection-based Generation of Electric Distribution Network Graphs

Amin Nikakhtar

Jun 2018 - Jun 2018
Texas Tech University
Now: Senior Data Scientist
Mayo Clinic

Doctoral thesis research proposal “Polynomial Optimization for Optimal Power Flow and Assured Generation”. Competitively selected under the U.S. DOE Office of Science Graduate Student Research Program

Till Koester

Aug 2016 - Jan 2017
University of Rostock, Germany
Now: Research Associate
The University of Rostock

High-Performance Simulations of Cell Biology Models on Modern GPUs
Co-developed artifacts

Maksudul Alam

Jan 2016 - May 2016
Virginia Tech
Now: Research Staff Member
Oak Ridge National Laboratory

“Computational Support for Cloning Simulations
Co-developed artifacts

Zhengchun Liu

Jan 2016 - May 2016
U. Barcelona, Spain
Now: Assistant Computer Scientist
Argonne National Laboratory

High Performance Agent-based and Discrete Event Simulations

Maksudul Alam

May 2015 - Aug 2015
Virginia Tech
Now: Research Staff Member
Oak Ridge National Laboratory

“Computational Support for Cloning Simulations
Co-developed artifacts

Masatoshi Hanai

Jan 2015 - May 2015
Durham University (UK), Tokyo Institute of Technology (Japan)
Southern University of Science and Technology

Exact-Differential Simulations” (mentored while at Durham University, UK
Co-developed artifacts

Advanced Scientific Computing Research

Fri, 07 Mar 2025 00:00:00 +0000

A snapshot of Kalyan Perumalla’s Program Manager roles in the Advanced Scientific Computing Research in the Office of Science, U.S. Department of Energy (March 1, 2025).

Program Management Areas

GEM Fellows

Sat, 01 Jan 2022 00:00:00 +0000

Fellows of the highly selective National GEM Consortium I had the privilege of hosting and mentoring through advanced research internships in my projects

In the past few years, the following brilliant GEM Fellows in the GEM Fellowship Program of the National GEM Consortium (National Graduate Degrees for Minorities in Engineering and Science, Inc.) worked with me on advanced research projects.

Kingsley Nwosu

Jun 2021 - Aug 2021
Virginia Tech
Now: Data Platform Engineer
Oak Ridge National Laboratory

Machine Learning and Artificial Intelligence applied to Cyber-physical Systems
Co-developed artifacts

Cora Chanel Richardson

Jun 2021 - Aug 2021
Duke University, Tufts University
Duke University

Quantum Optimization and Reversible Computing applied to Electric Grid Resilience
Co-developed artifacts

Alejandro Vega Nogales

Jun 2019 - Aug 2019
Indiana University, University of Puerto Rico
Now: Computer Science & Engineering PhD student
University of Notre Dame

Machine Learning Techniques applied to Cyber-Physical Systems
Co-developed artifacts

Undergraduate Interns

Sat, 01 Jan 2022 00:00:00 +0000

Brilliant undergraduate students who worked with me as interns in multiple projects over the years, some even winning us best paper awards

Matthew Street

Jun 2010 - Aug 2010
Middle Tennessee State University

Visualizing Rollbacks in Parallel Discrete Event Simulations
Co-developed artifacts

Brandon Aaby

Jun 2008 - Aug 2008
Maryville College

High Performance Hybrid Computing using Networks of Processors and GPGPUs
Co-developed artifacts

Clayton Thurmer

Jan 2008 - May 2008
Oberlin University

Discrete Event Simulator Benchmarking
Co-developed artifacts

Brandon Aaby

Jun 2007 - Dec 2007
Maryville College

Social Science Simulations on GPGPUs
Co-developed artifacts

Patrick Wilkerson

Jun 2007 - Aug 2007
Austin Peay State University

Parallel FDTD as Asynchronous Speculative Execution
Co-developed artifacts

John Wright

Jun 2007 - Aug 2007
Mercer University

Reversibility of Computational Methods
Co-developed artifacts

Kathleen Abercrombie

Jun 2006 - Aug 2006
Georgia Tech

Cut Selection in Sensor Network Optimization
Co-developed artifacts

Eric Beier

Jun 2006 - Aug 2006
Texas A&M

Federated Optimization of Sensor Networks
Co-developed artifacts

Alfred Park

Jun 2006 - Aug 2006
Georgia Tech

Integrated Analysis of Sensor Networks
Co-developed artifacts

Jason Roop

Jun 2006 - Aug 2006
N. Carolina Central

Social Network Simulation using GPGPUs
Co-developed artifacts

High School Interns

Sat, 01 Jan 2022 00:00:00 +0000

Powerful talent blossoms early, like these budding technologists I had the pleasure of mentoring before they went onto college and launching startups

Charles Elliott (CJ) Johnson

May 2015 - Aug 2015
L&N STEM Academy

Message Passing Interface in JavaScript
Co-developed artifacts

Melissa Yu

Jun 2013 - Aug 2013
Farragut High School

Memory-Efficient Reversible Algorithms for Integer Square Root Computation
Co-developed artifacts

Programming Teams

Sat, 01 Jan 2022 00:00:00 +0000

My associations in programming contests – team members I coached at Georgia Tech, and my own coach when I was a team member at UCF

My Coach

David Van Brackle

Aug 1991 - May 1993
University of Central Florida)
Now: Contest Chief Judge (multiple years)
ISX

ICPC Page | ACM Intercollegiate Programming Contest Coach - Southeast Regionals

My own coach at the University of Central Florida (UCF) was David Van Brackle. UCF is well known in the US Southeast Region as nearly always at the top in ACM Intercollegiate Programming Contests. Our faculty advisor was Dr. Ali Orooji. My team placed 2nd and 8th in the Southeast Region in 1991 and 1992, respectively.

After completing my Master’s, I moved from UCF to Georgia Tech for my PhD work, where I started coaching the Georgia Tech teams and took them to a few top spots (Southeast Regional toppers, finalists, and to two World Finals contests in Nashville, USA and Eindhoven, Netherlands). After a few years of coaching the Georgia Tech teams, I had to stop coaching to focus on my PhD research. At that time, David was in Atlanta, and I invited him to take over as the Georgia Tech coach, which he did and took them to great heights.

Members I Coached

Below are a few great programmers who happened to be in the teams I coached at Georgia Tech.

Vu Pham

Jan 1995 - Jan 1998
Georgia Institute of Technology
Now: Data Science Consultant
Chameleon Collective

ACM Intercollegiate Programming Contest - Regionals and World Finals (Eindhoven)

Peter Lindstrom

Jan 1995 - May 1995
Georgia Institute of Technology
Now: Computer Scientist
Lawrence Livermore National Laboratory

ACM Intercollegiate Programming Contest - Regionals and World Finals (Nashville)

Brian McNamara

Jan 1995 - May 1995
Georgia Institute of Technology

ACM Intercollegiate Programming Contest - Regionals and World Finals (Nashville)

Corey Rhoden

Jan 1995 - May 1995
Georgia Institute of Technology

ACM Intercollegiate Programming Contest - Regionals and World Finals (Nashville)

David Minnen

Jan 1996 - Aug 1997
Georgia Institute of Technology
Now: Software Engineer
Google

ACM Intercollegiate Programming Contest - Regionals and World Finals (Eindhoven)

Kalyan Perumalla

Sat, 01 Mar 2025 00:00:00 +0000

Competitive Portfolios for Advanced Scientific Computing Research: Data Management and Visualization

Sat, 01 Feb 2025 00:00:00 +0000

Solicitation PDF

Original at OSTI
Cached local copy

Selected Pages

Selected Extracts

The SC ASCR program hereby announces its interest in advanced scientific computing research portfolios for accelerating discovery and innovation in support of the DOE mission. ASCR seeks to invest in DOE National Laboratory-led portfolios that balance long-term, high-impact research along with the ability to aggressively respond to, and take advantage of, emerging science and technology trends. The ASCR Computer Science (CS) research program [1] supports long-term, basic research that enables computing and networking at extreme scales and the understanding of extreme-scale and complex data from both simulations and experiments. ASCR, in tandem with industry and others, has made highly successful investments to ensure U.S. leadership in high performance computing (HPC), which resulted in Exascale systems that are enabling scientific discovery and decision support through data integration, simulation and modeling [2].

To ensure continued leadership in delivering on the promise of computational science, and drive innovation in energy-efficient and versatile HPC for science, ASCR seeks to invest in DOE National Laboratory-led portfolios that:

Support long-term, high-impact CS research,
Aggressively respond to, and take advantage of, emerging science and technology needs and trends including Artificial Intelligence (AI), and
Collaborate with a diverse community of the most-promising academic and industry partners

SUPPLEMENTARY INFORMATION

Scientific research driven by Artificial Intelligence (AI)-enabled technologies is not only making scientists more productive but promises to change how scientists find the most-promising ideas to investigate in the future [3]. This requires deep changes in the methods available, and algorithms developed, to store, search, retrieve, analyze, and visualize scientific data. Past efforts which focused primarily on storing and analyzing data quickly only in specifically-anticipated contexts are giving way to discovery-optimized techniques which prioritize supporting AI- enabled investigation and the aggregation of curated data sets of many kinds.

In this context, ASCR seeks innovative research with vision beyond its current investments in HPC data management, storage [4], and scientific visualization [5] that will help enable the development of the next-generation energy-efficient and capable computing systems [6] and approaches enabling accelerated scientific discovery. This can include the use of new hardware, software, algorithms, and other related technologies that are currently at early stages of development.

While proposed work can leverage software from prior research efforts where they add significant value, ASCR is primarily looking for new research efforts in scientific data management, storage, and visualization. These efforts should build on the best available open platforms and befit the future of energy-efficient AI-driven scientific discovery where data management and visualization are fast, efficient, and flexible.

Exploratory Research for Extreme-Scale Science (EXPRESS)

Sat, 01 Feb 2025 00:00:00 +0000

Solicitation PDF

Original at OSTI
Cached local copy

Selected Pages

Selected Extracts

B) Local and Campus-Area Quantum Networking for Next Generation Parallel Quantum Computing

Quantum networking involves effective communication of quantum information among geographically distributed quantum systems, separated by short or long distances. Sources of quantum information in the network communication include qubits used in different forms of quantum computing or output from various quantum sensing devices.

This topic seeks advancements in quantum networking over short distances to enable parallel quantum computing within a building and integration of quantum information sources to storage and quantum computing across a campus area. The interconnection of different co-located quantum computing systems is aimed at increasing the scale of quantum computation (e.g., aggregate number of qubits) and at progressing towards an architecture of flexible connectivity of quantum devices across a laboratory or university campus, potentially composing heterogeneous quantum computing hardware, including broadening of networking from exchange of physical qubits to logical qubits.

Research Area

The specific aim of this topic is to support quantum science needed to effectively scale quantum computing and enable flexible exchanges of coherent quantum information via locally networked heterogeneous quantum systems. Proposals must address one or more research advancements in the aforementioned directions in local and campus-area quantum networking. Research must be aimed at advancing our understanding of aspects, such as core concepts, devices, architectures, integration, and interfaces, that are necessary for a quantum counterpart to the current infrastructures of classical local and campus area networks within the scientific and other facilities.

Scientific Discovery through Advanced Computing (SciDAC) Institutes

Sat, 01 Feb 2025 00:00:00 +0000

Solicitation PDF

Original at OSTI
Cached local copy

Selected Pages

Selected Extracts

The DOE Office of Science (SC) program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in receiving proposals from large multi-disciplinary and multi- institutional teams for the Scientific Discovery through Advanced Computing (SciDAC) Institutes, as part of the SC SciDAC Program.

The ASCR program’s mission (https://science.osti.gov/ascr and https://science.osti.gov/ascr/Community-Resources/Program-Documents) is to advance applied mathematics and computer science; deliver the most sophisticated computational scientific applications in partnership with disciplinary science; advance computing and networking capabilities; and develop future generations of computing hardware and software tools for science and engineering, in partnership with the research community.

The SciDAC Program (https://www.scidac.gov) was initiated in 2001 as an SC-wide program to dramatically accelerate progress in scientific discovery via advanced computing. SciDAC consists of ASCR-funded SciDAC Institutes and SciDAC Partnerships co-funded by ASCR with SC and other DOE programs. Since its inception, the SciDAC program has been recognized as a leader in enabling scientific discoveries that would not have been possible without the deep collaborations between discipline scientists, applied mathematicians, and computer scientists.

This Announcement seeks proposals for SciDAC Institutes, which are a key part of the SciDAC program. Specifically, this Announcement is for the selection for award of proposals for two types of SciDAC Institutes: Computer Science Institute (CSI), and Applied Mathematics Institute (AMI).

Early Career Research Program (ECRP)

Wed, 01 Jan 2025 00:00:00 +0000

Solicitation PDF

Original at OSTI
Cached local copy

Selected Pages

Selected Extracts

Computer Science: Systems

Programming Models and Environments: Innovative programming models for developing applications on next-generation platforms, exploiting unprecedented parallelism, heterogeneity of memory systems (e.g. non-uniform memory access [NUMA], non-coherent shared memory, high-bandwidth memory [HBM], scratchpads, and heterogeneity of processing (e.g. graphics processing units [GPUs], field- programmable gate arrays [FPGAs], coarse-grained reconfigurable architectures [CGRAs], other types of accelerators, big-small cores, processing in memory, and near memory, etc.), with particular emphasis on making it easier to program at scale. All phases of the software-development cycle are relevant, including but not limited to, design, implementation, verification, optimization, and integration. Particularly welcome are methods that infuse artificial intelligence/machine learning into the programming environment.
Operating and Runtime Systems: System software that provides intelligent, adaptive resource management and support for highly-parallel software and workflow-management systems, and that facilitates effective and efficient use of heterogeneous computing technologies, including diverse execution models, processors, accelerators, memory, and storage systems. Target workloads include modeling and simulation, data analysis, and the processing of large- scale, streaming data from experiments.
Performance Portability and Co-design: Methods that support performance portability, which provides the ability to efficiently use diverse kinds of hardware platforms with minimal changes to the application source code, and/or hardware/software co-design, which is a method for designing and/or adapting both hardware and software design as part of a holistic process. These methods include automated and semi-automated refinements from high-level specification of an application and/or hardware design to low-level code, optimized when compiled and/or, for software, at runtime, to different HPC platforms. The focus is on enabling performance portability of, and/or the design of future hardware for, applications developed for extreme-scale computing and beyond.
Memory-Aware Systems: Advances in memory technologies are creating new opportunities and challenges where it is unclear how to best introduce or abstract memory awareness and composition. Memory is evolving in highly asymmetric and distributed directions, with new industry standards greatly expanding memory sharing and capacities to much larger sizes, largely in backward-compatible system architectures. Research is needed to uncover new possibilities for solving larger scientific computing problems with such highly asymmetric and distributed memory architectures. Innovations in algorithms, software interfaces, programming languages and models are needed to also effectively exploit new processing-in-memory architectures that are emerging as a relatively newer paradigm for scientific computing. Memory safety needs to be revisited in new research aimed at a more fundamental level of programming languages, runtimes, and operating systems, considering the multi-developer and shared nature of modern scientific programming eco-systems. The smoothening of the spectrum from volatile to non- volatile memories needs to be investigated for revisiting out-of-core algorithms to expand the limits of scientific computing. On-the-fly compression and decompression needs investigation for increasing the problem sizes without detriment to performance. The intersection of machine learning with memory systems opens the potential for new solutions, including smarter ML- informed cache prefetching and replacement policies potentially customizable for specific scientific applications via signatures and other mechanisms.

Applications are not restricted to a single Systems topic above and may span all of them, provided the scope of work remains appropriate for the program.

National Quantum Information Science Research Centers

Wed, 01 Jan 2025 00:00:00 +0000

Solicitation PDF

Original at OSTI
Cached local copy

Selected Pages

Selected Extracts

DOE SC will provide support for Centers that will accelerate the transformational advances in basic science and quantum-based technology needed to assure continued U.S. leadership in QIS, consistent with the National Quantum Initiative. The purpose of these Centers will be to push the current state-of-the-art science and technology toward realizing the unique power of harnessing quantum phenomena in computing, communication, and sensing. The multi-disciplinary nature of the field, the need for precise control of complex physical systems to observe and utilize quantum behavior, and the potential for substantial economic consequences are the major drivers of the National Quantum Initiative. The Centers, coupled with a robust core research portfolio stewarded by the individual SC programs, will create the ecosystem needed to foster and facilitate advancement of QIS with public benefits in national security, economic competitiveness, and leadership in scientific discovery.

…

Quantum Communication
- Understanding and meeting the requirements for scalable and adaptable quantum network infrastructures designed to support the transmission of diverse types of quantum information
- Fundamental limits on information transfer in quantum systems
- Techniques and tools to address transduction and network integration (architectures, protocols, control, heterogeneous device integration and interoperability, and so on, including coexistence of quantum and classical communications)
- Techniques to support in-situ computation within photonic or other quantum architectures or devices for quantum communications
- Benchmarking techniques for performance measurement and system characterization, and their application to both commercially-available and testbed systems
- Facilities to support network development and testing
Quantum Computing and Simulation
- …
Quantum Devices and Sensors
- …

Continuation of Solicitation for the Office of Science Financial Assistance Program

Tue, 01 Oct 2024 00:00:00 +0000

Announcement

Press Release

Solicitation PDF

Original at OSTI
Cached local copy

Selected Pages

Selected Extracts

Network-Offloaded Acceleration for Distributed/Parallel Computing: Programmable and computation-enabled network interfaces present the opportunity to exploit computational power closer to the network to complement the capabilities of CPUs, GPUs, and other computational components. Note that the programmable network interfaces include both edge accelerators as well as devices in core interconnects in parallel platforms or transport planes in distributed settings. Application behavioral information may be exploited, both in terms of dynamic learning as well as mathematically predefined primitives such as distributed reductions and other offloaded synchronization operations. New methods, algorithms, software, and interfaces are needed to effectively exploit asynchronous and autonomous capabilities of network hardware beyond traditional data-transfer functionalities. Of interest are new conceptual approaches, algorithmic support, application programming interfaces, and use cases in HPC scientific applications.
Computer Science Fundamentals Accounting for Thermodynamics and Energy: Unprecedented levels of modern computation, including areas such as artificial intelligence and machine learning (AI/ML) training, have now made computation a very large consumer of energy in the Nation and the world. Much of modern computer science, and the understanding it provides regarding the fundamental properties of algorithms, does not account for the underlying thermodynamic and information-theoretic reality of computation. As “Beyond Moore” devices are explored along with their corresponding ultra-efficient computer architectures, and the programming paradigms appropriate for these new computing technologies, a better understanding is needed of both potential ultra-efficient computer architectures and the energy-aware properties of algorithms executed on them. Ultra-efficient computer architectures include, but are not limited to, those based on reversible and asymptotically-adiabatic approaches. Investigations combining thermodynamics and information theory, computer architecture, reversible computing and algorithmic properties are sought to advance our ability to design new, energy-efficient approaches to scientific computation.
Memory-Aware Systems: Advances in memory technologies are creating new opportunities and challenges where it is unclear how to best introduce or abstract memory awareness and composition. Memory is evolving in highly asymmetric and distributed directions, with new industry standards greatly expanding memory sharing and capacities to much larger sizes, largely in backward- compatible system architectures. Research is needed to uncover new possibilities for solving larger scientific-computing problems with such highly asymmetric and distributed memory architectures. Innovations in algorithms, software interfaces, programming languages and models are needed to also effectively exploit new processing-in-memory architectures that are emerging as a paradigm for scientific computing. Memory safety needs to be revisited in fundamental research on programming languages, runtimes, and operating systems, considering the multi-developer and shared nature of modern scientific programming eco- systems. The smoothening of the spectrum from volatile to non-volatile memories needs to be investigated for revisiting out-of-core algorithms to expand the limits of scientific computing. On-the-fly compression and decompression needs investigation for increasing the problem sizes without detriment to performance. The intersection of machine learning (ML) with memory systems opens the potential for new solutions, including smarter ML-informed cache prefetching and replacement policies potentially customizable for specific scientific applications via signatures and other mechanisms.
Quantum Networking: This topic involves innovative research in quantum networking concepts, systems, and protocols by which quantum networking applies in scientific discovery, including, but not limited to, distribution of quantum information from sensors, quantum networking in support of interconnected or scalable quantum computing systems, and blind/cloud quantum computing. Networking can span heterogeneous systems or homogeneous systems (such as all-photonic) and parallel quantum processing (in co-located or local-area settings) and distributed quantum communications (at metropolitan or wide-area scales). Possible topics include quantum networking areas as presented in “Report for the ASCR Workshop on Basic Research Needs in Quantum Computing and Networking,” https://doi.org/10.2172/2001045.

Basic Research Needs in Analog Computing in Science

Wed, 11 Sep 2024 00:00:00 +0000

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Conventional digital computing faces fundamental physical limits: large scale computing systems already consume tens of Megawatts of power, Dennard scaling has ended, and data movement costs dominate application performance. Next generation experimental facilities generate data at rates that overwhelm conventional processing and demand real-time analysis at the source. Analog computing, which exploits the continuous dynamics of physical systems to perform computation, promises a transformative path toward orders-of-magnitude gains in energy efficiency and time-to-solution for scientific workloads.

The workshop’s findings also identified several cross-cutting challenges underpinning all six PRDs, including a mathematical theory of continuous computation under noise, programming abstractions and compiler toolchains, and community benchmarking infrastructure, emphasizing the needs for a coordinated, multi-faceted effort to enable rapid progress in the area. The research directions outlined in the workshop report aim to guide the development of energy-efficient analog computing technologies that can support future scientific discoveries and address the growing energy demands of scientific computing and artificial intelligence.

Reaching a New Energy Sciences Workforce (RENEW)

Fri, 01 Mar 2024 00:00:00 +0000

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This subprogram supports research that enables computing and networking at extreme scales and the understanding of extreme-scale and complex data from both simulations and experiments. It aims to make high performance scientific computers and networks highly productive and efficient to solve scientific challenges while attempting to reduce domain science application complexity as much as possible. The research is positioned in the context of multiple challenges across several areas such as sharp increases in the heterogeneity and complexity of computing systems and the need to integrate simulation, data analysis, and other tasks seamlessly and intelligently into coherent and usable workflows. Research in computer science is also motivated by major disruptive developments in artificial intelligence, machine learning, natural language processing, large language modeling, all of which offer the potential to significantly advance scientific discovery through novel hardware, software, theory, and algorithms for scalable computing.

Areas of interest in this subprogram include the following, as relevant to SC and DOE priority applications:

Data: Data Management; Data analysis; Storage Systems; I/O, Visualization
Computing Paradigms: Continuum Computing; Energy-efficient Computing; Heterogeneous Computing and Acceleration
Systems Research: Programming Models, Environments, and Portability; Scalable Parallel Operating and Runtime Systems; Scalable Middleware
Software: Performance Portability and Co-Design; Scientific Developer Tools and Automation for High Productivity and Assurance
Distributed Systems: Complex Workflows, Distributed Scheduling and Resource Management; Advanced High-Speed Networking; Edge Computing; Experiment- Computation Integration
Standardization: International Standards and Interoperability

Advancements in Artificial Intelligence for Science

Thu, 01 Feb 2024 00:00:00 +0000

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The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in basic computer science and applied mathematics research in the fundamentals of Artificial Intelligence (AI) for science. Specifically, advancements in this area are sought that can enable the development of:

Foundation models for computational science;
Automated scientific workflows and laboratories;
Scientific programming and scientific-knowledge-management systems;
Federated and privacy-preserving training for foundation and other AI models for science; and
Energy-efficient AI algorithms and hardware for science.

The development of new AI techniques applicable to multiple scientific domains can accelerate progress, increase transparency, and open new areas of exploration across the scientific enterprise.

Research Area 1: …

…

Research Area 2: AI Innovations for Scientific Knowledge Synthesis and Software Development

The state-of-the-art in knowledge synthesis and programming tools are changing rapidly, fueled by AI Large Language Models (LLMs) trained on text, source code, and other data sources. New AI-driven tools are currently not trustworthy; do not systematically understand mathematical and physical principles; cannot properly ingest and understand scientific literature and data; and do not produce consistent, verified, uncertainty-quantified, reproducible results. In addition to addressing those challenges, there may be particular advantages in such tools building up knowledge and context over many interactions with a user or group of users. However, incremental training of AI systems over long time horizons, and the representation of knowledge in AI systems robust to changes in the underlying AI models, remain critical challenges. This research area seeks fundamental advancements in knowledge synthesis and programming tools for science. Moreover, realizing AI systems that can truly understand, and assist with, all aspects of the scientific process requires innovation in many areas, including multimodality, tool use, deeper reasoning and planning, memory, and external interaction. For additional background, see Chapter 2, “AI Foundation Models for Scientific Knowledge Discovery, Integration, and Synthesis,” Chapter 6, “AI for Programming and Software Engineering,” Chapter 12, “Mathematics and Foundations,” and Chapter 14, “Data Ecosystem,” of the AI For Science, Energy, and Security report [1].

Additionally, investigations into AI-driven tools for science should be conceptualized accounting for the iterative and collaborative processes that define modern science and scientific-software development. Accordingly, research proposed in this area is encouraged to address the relevant Priority Research Directions (PRDs) from the Basic Research Needs in The Science of Scientific Software Development and Use report [6], which are PRD 1, “Develop next-generation tools to enhance developer productivity and software sustainability,” PRD 2, “Develop methodologies and tools to comprehensively improve team-based scientific software development and use,” and PRD 3, “Develop methodologies, tools, and infrastructure for trustworthy software-intensive science.

Methods proposed for investigation should use any appropriate techniques that might be necessary to accomplish their goals, including, but not limited to, machine learning, natural- language processing, formal reasoning, instrumentation, data management, and compiler technology. The sustainability and explainability of scientific software are critically important to the scientific process, and as a result, particular consideration should be given to maximizing the extent to which human programmers understand and/or trust the outputs of these methods.

Research Area 3: …

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Research Area 4: …

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Research Area 5: …

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Competitive Portfolios for Advanced Scientific Computing Research

Mon, 01 Jan 2024 00:00:00 +0000

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To ensure continued leadership in delivering on the promise of computational science, and drive innovation in energy-efficient and versatile high-performance computing for science, ASCR seeks to invest in DOE National Laboratory-led portfolios that:

Support long-term, high-impact research,
Aggressively respond to, and take advantage of, emerging science and technology trends, and
Collaborate with a diverse community of the most-promising academic and industry partners.

Research Proposals: Each Laboratory is limited to leading one proposal in response to this Announcement. The Principal Investigator (PI) must be a Laboratory division director or a manager with equivalent supervisory responsibilities. The proposal narrative (at most 30 pages) must provide a Laboratory vision and management plan for the portfolio of capabilities stemming from the proposed research and development in scientific computing. The narrative is comprised of one or more research Thrusts. Each Thrust must have a Laboratory Senior/Key Personnel (SKP) as the Lead along with other SKPs and researchers. Overall, the proposal must describe the research Thrusts and integration Tasks needed to enable new scientific computing- based capabilities that address national priorities in energy, the environment, and national security. The proposal should describe how the overall vision and each Thrust take advantage of the responding Laboratory’s, and each partnering institution’s, distinctive expertise and capabilities.

Research Thrusts: A Thrust is a distinct, focused area of basic research in scientific computing.

Applied Mathematics: Single-facet Thrusts require and build on research expertise in a core area such as s linear algebra and nonlinear solvers, discretization and meshing, multi-scale mathematics, discrete mathematics, optimization, complex systems, emergent phenomena, and applied analysis methods including but not limited to analysis of large- scale data, uncertainty quantification, and error analysis, or related topics. [4, pg. 281]
Computer Science: Single-facet Thrusts require and build on research expertise in a core area such as programming languages, high-performance computing tools, peta- to exa- scale scientific data management and scientific visualization, distributed computing infrastructure, programming models for novel computer architectures, and automatic tuning for improving code performance, or related topics. [4, pg. 281]
Advanced Computing Technologies and Testbeds: Access to resources to test and develop new tools, libraries, languages, etc. is an important enabling capability [4, pg. 281]. Thrusts focused on the establishment and development of testbeds1 that offer promising paths to versatile energy-efficient computing, addressing among other challenges, the data storage and movement requirements of artificial intelligence and simulation workloads may be proposed in response to this Announcement. Computing hardware should be interpreted broadly to include computational, memory, networking,

Exploratory Research for Extreme-Scale Science (EXPRESS)

Mon, 01 Jan 2024 00:00:00 +0000

Awards

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Extreme-scale science recognizes that disruptive technology changes are occurring across science applications, algorithms, computer architectures and ecosystems. Recent reports point to emerging trends and advances in high-end computing, massive datasets, visualization, and artificial intelligence on increasingly heterogeneous architectures. Significant innovation will be required in the development of effective paradigms and approaches for realizing the full potential of scientific computing from emerging technologies. Proposed research should not focus on a specific science use case, but rather on creating the body of knowledge and understanding that will inform future advances in extreme-scale science. Consequently, the funding from this FOA is not intended to incrementally extend current research in the area of the proposed project. It is expected that the proposed projects will significantly benefit from the exploration of innovative ideas or from the development of unconventional approaches.

Exploratory Research for Extreme-Scale Science (EXPRESS) opportunities exist for the following research topics:

A. Harnessing Technology Innovations to Accelerate Science through Visualization
B. Scalable Space-Time Memories for Large Discrete/Agent-Based Models
C. Neuromorphic Computing
D. Advanced Wireless
E. Quantum Hardware Emulation

Applications submitted in response to this FOA must substantially address one among the preceding list of research topics.

A Flexible OT Testbed for Evaluating On-Device Implementations of IEC-61850 GOOSE

Fri, 01 Sep 2023 00:00:00 +0000

Open Access:
- Journal: https://www.sciencedirect.com/science/article/pii/S1874548223000318.
- PDF: https://www.sciencedirect.com/science/article/pii/S1874548223000318/pdfft?md5=4606c01cd9fbd3efc0e6b73a074b8910&pid=1-s2.0-S1874548223000318-main.pdf.

Basic Research Needs in Quantum Computing and Networking

Tue, 11 Jul 2023 00:00:00 +0000

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In July 2023, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science (SC)convened the Workshop on Basic Research Needs in Quantum Computing and Networking, where major opportunities and challenges were discussed and identified. Before the workshop, a pre-workshop report was prepared to seed potential research themes, and community input was solicited in the form of brief position papers. These formed the basis of panel and discussion sessions at the workshop. This report summarizes the findings of the workshop.

Artificial Intelligence Tools for Catalyzing Interdisciplinary Science (SBIR)

Sat, 01 Jul 2023 00:00:00 +0000

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This topic is focused on new artificial intelligence (AI) tools that enhance the productivity of scientists and engineers when making use of scholarly publications and engaging in interdisciplinary interactions in the areas of science and engineering supported by SC. While scientists are deeply knowledgeable in their areas of expertise, they are often not as highly informed in other important disciplines. For example, experts in several subareas of high-performance computing are not as deeply knowledgeable about the terminology, concepts, and state of the art in other areas such as biological sciences or high energy physics. The advent of new service-oriented access to technology that is based on large language models (LLM), which may include multi- modal data, has now opened the possibility of utilizing LLM-based commercial services to enable new interdisciplinary synergy. These services can now be envisioned to be used to digest large amounts of scientific publications and documentation across disciplines and enable interdisciplinary interactions that were not conceivable before.

Against this backdrop, grant applications are sought on the topic of “AI Tools for Catalyzing Interdisciplinary Science.” This topic will be focused on increasing the synergy among disciplines supported by the Office of Science. For example, this would include AI-based tools that can catalyze the interactions among scientists in nuclear physics and material sciences. Innovative methods are needed to assimilate the scientific publication corpus of two or more disciplines and enable scientists in any of those disciplines to collate scientific ideas, concepts, questions, and solutions from the other disciplines.

Included in scope is the integration with commercial AI services (Google, Microsoft, OpenAI, etc.) and open/commercial sources of publications and other data. Proposed approaches must display a short-term path to success and commercial viability. The proposed work should include a plan to perform demonstration activities with scientists and demonstrate verification and validation of results, including data validation.

Grant applications focused on the following will be considered out of scope:

Tools that address less than two scientific disciplines in Office of Science research areas.
Tools that build LLM-based services from scratch.
Security and hardening of LLM.

a. Interdisciplinary Training and Interfaces

Applications responsive to this subtopic will address the challenge of ingesting a multi-modal corpus of scientific publications of two or more scientific disciplines in Office of Science research areas into a knowledgebase to be built using LLM-based services.

Additionally, applications may address the creation of modern interfaces with natural language-based prompt- and-response support necessary for scientists to interact with the LLM back-ends of interdisciplinary knowledgebases.

Digital-Twin Capabilities for Science Network Infrastructures (SBIR)

Sat, 01 Jul 2023 00:00:00 +0000

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Digital twins are an emerging area of modern science where a physical object (i.e., device, process, or infrastructure) is paired with a digital (virtual) version of that same object. Operations of the physical object can generate data to validate the virtual objects behavior while the virtual object allows rapid exploration of input parameters that might damage the physical object. It is this close interaction between the physical and virtual object that makes this digital-twin environment so productive.

DOE has a long history of building and operating high performance physical network infrastructures. The Energy Science Network (ESnet) supports the Office of Science lab complex and it also peers with other research and education networks (RENs) both domestically and internationally. ESnet also operates an internal 100G SDN network testbed and the NSF funded FABRIC external network testbed. Specifically:

a) The ESnet 100G Software-Defined Networking (SDN) testbed’s objective is to provide network researchers with a realistic environment for testing. The current testbed enables 100G application / middleware experiments in addition to Science DMZ and SDN control/data plane experiments.

b) FABRIC: The National Science Foundation (NSF) collaboration is building a national research infrastructure that will enable the computer science and networking community to develop and test novel architectures that could yield a faster, more secure Internet. What is missing is a virtual companion, a digital twin, to these testbeds.

This topic solicits applications that would create the network simulation capabilities that would accurately and reliably duplicate the operational and performance capabilities of these testbeds creating their digital twin.

Mixed Integer Solver Technology for Accelerated Computing Systems (SBIR)

Sat, 01 Jul 2023 00:00:00 +0000

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This topic is focused on specific technologies that are required to advance the state of accelerated computing applied to mixed integer programming (MIP) problems arising in scientific applications. The primary focus of this topic is on the computer science needed to address the challenges in manifestation of scalable, distributed (multi-node) algorithmic techniques and not on combinatorial theory development.

MIP problems underlie many important application areas of interest to DOE, including biological systems, transportation networks, electric grids, and user facility infrastructures. While significant advances have been made in the theory and implementation of MIP solver methods on conventional central processing unit (CPU)- based hardware, new advances are necessary to fully utilize the DOE investments in accelerated computing platforms such as graphical processing unit (GPU)-based computers and high-performance computing systems. Preference may be given to applications that leverage existing ASCR software investments.

Grant applications focused on the following will be considered out of scope:

Cut generation, cut storage, cut manipulation methods,
Column generation methods,
Theory,
Fragments of technology that are isolated and cannot be demonstrated as part of a working MIP solver on standard problems such as found in the MIPLIB series, and
Solutions that cannot be demonstrated to run on GPU-based accelerators used in current or planned supercomputing systems of DOE leadership computing facilities.

Grant applications are sought in the following subtopics:

a. Efficient Distributed Tree Management This topic is focused on tree management that arises in branch-and-bound or branch-and-cut (B&C) methods to MIP solution methods. Research must focus on efficient representation and encoding of the B&C tree on accelerated memory hierarchies and address the challenges of efficient tree node movement, including exploitation of direct memory access (DMA) of accelerated memory across high-speed networks. Methods must solve the problem of scalable and efficient manipulation of B&C tree nodes. This includes the ability to query the quality of linear program relaxation, node ancestor identification, node deletion, and updates to the node data.

b. Efficient Linear Program Relaxation Solution Implementations of interior or exterior point methods must be developed specifically optimized for accelerated hardware using single-instruction-multiple-thread (SIMT) control flows or reconfigurable field programmable gate arrays (FPGAs). Methods must build on existing or new sparse and dense solvers and capable of static or dynamic (on-the-fly) choice of sparse versus dense solver based on the density of matrices20 Return to Table of Contents encountered in the input scenarios. Applicants may propose solving the linear program relaxations of MIP problem matrices that either fit entirely within a single node’s memory or large problem sizes where matrices do not fit within a single node’s memory but span multiple node memories. Milestones must aim to solve the relaxations of root nodes in increasing fractions (10%, 50%, 75%, 90%) of the problems in the MIPLIB 2017 problem set (with priming or probing methods not necessarily applied to the root problems).

Determining the Most Significant Metadata Features to Indicate Defective Software Commits

Tue, 23 May 2023 00:00:00 +0000

Scientific Enablers of Quantum Communications

Sat, 01 Apr 2023 00:00:00 +0000

Awards

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The U.S. Department of Energy (DOE) Office of Science (SC) program in Advanced Scientific Computing Research (ASCR) announces its interest in receiving proposals from DOE National Laboratories to advance scientific understanding of the fundamental elements needed to enable novel capabilities for the Nation in quantum communications.

ASCR envisions DOE National Laboratories and User Facilities that exchange quantum information acquired by quantum sensors, analyzed by quantum computers, and shared among collaborative scientific experiments. This research infrastructure would connect distributed quantum computers that simulate complex scientific processes inaccessible to computational platforms of today, integrate quantum sensors that promise measurements of unprecedented precision, and address previously inaccessible scientific questions of importance across the DOE SC programs.

Exploitation of the principles of quantum physics that offer scientific discovery unreachable through classical approaches demands fundamental advances that transmit qubits over long distances, control their route, detect and correct errors, and interface with peer quantum devices. ASCR is seeking proposals for research into quantum networking devices that could become components of quantum network repeaters, associated error correction and mitigation techniques, and quantum network architecture, stack and communication protocols.

The proposed research must lead to advances in quantum networking that can accommodate increasing numbers of end point connections and increasing ranges, from small research teams to large research collaborations, and from local to regional to national distances. ASCR envisions local quantum networks that connect quantum-enabled research infrastructure, which may scale to become part of a future quantum internet that connects DOE SC User Facilities with each other and with remote quantum-enabled research infrastructure, to accelerate scientific discovery.

Impact of Grammar on Language Model Comprehension

Mon, 20 Feb 2023 00:00:00 +0000

https://www.computer.org/csdl/proceedings-article/icnc/2023/10074239/1LKwLebVPGw

A Testbed for Evaluating Performance and Cybersecurity Implications of IEC-61850 GOOSE Hardware Implementations

Sun, 08 Jan 2023 00:00:00 +0000

Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid

Thu, 20 Oct 2022 00:00:00 +0000

ExaSGD Performance Profiling

Tue, 28 Jun 2022 00:00:00 +0000

View the report here.

For the context for this report, please see the ExaSGD ECP Project.

Design of a Novel Information System for Semi-Automated Management of Cybersecurity in Industrial Control Systems

Wed, 22 Jun 2022 00:00:00 +0000

Article is available as Open Access

An Accuracy-Maximization Approach for Claims Classifiers in Document Content Analytics for Cybersecurity

Wed, 15 Jun 2022 00:00:00 +0000

Open Access: https://www.mdpi.com/2624-800X/1/4/31.

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Wed, 11 May 2022 00:00:00 +0000

https://www.osti.gov/servlets/purl/1855247

Extending the Naming Game in Social Networks to Multiple Hearers per Speaker

Mon, 18 Apr 2022 00:00:00 +0000

ZeroIn: Characterizing the Data Distributions of Commits in Software Repositories

Sat, 16 Apr 2022 00:00:00 +0000

Using Machine Learning Towards Early Flagging of Potentially Buggy Software Commits

Fri, 08 Apr 2022 00:00:00 +0000

DarkNet Cyber Resilience

Tue, 22 Mar 2022 00:00:00 +0000

Credits

Sat, 01 Jan 2022 00:00:00 +0000

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Sat, 01 Jan 2022 00:00:00 +0000

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Publications

Sat, 01 Jan 2022 00:00:00 +0000

Terms

Sat, 01 Jan 2022 00:00:00 +0000

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Characterizing the Distributions of Commits in Large Source Code Repositories

Wed, 15 Dec 2021 00:00:00 +0000

CyBERT: Cybersecurity Claim Classification by Fine-Tuning the BERT Language Model

Thu, 04 Nov 2021 00:00:00 +0000

Open Access: https://www.mdpi.com/2624-800X/1/4/31.
This article belongs to the Special Issue Machine Learning and Data Analytics for Cyber Security.

Fast GPU-Based Generation of Large Graph Networks From Degree Distributions

Mon, 01 Nov 2021 00:00:00 +0000

https://www.frontiersin.org/articles/10.3389/fdata.2021.737963

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8663089/

Design Considerations for GPU-based Mixed Integer Programming on Parallel Computing Platforms

Mon, 09 Aug 2021 00:00:00 +0000

https://dl.acm.org/doi/abs/10.1145/3458744.3473366

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs: Fast What–If Analyses of Localized and Dynamic Effects

Sun, 01 Aug 2021 00:00:00 +0000

https://link.springer.com/article/10.1007/s41745-021-00253-1

A Case Study in Simulation Methods for Power Electronic Circuits

Mon, 19 Jul 2021 00:00:00 +0000

https://ieeexplore.ieee.org/abstract/document/9552085

On Some Confluences of Computing and Security in Theory and Practice

Fri, 21 May 2021 00:00:00 +0000

Trust-but-Verify in Cyber-Physical Systems

Wed, 28 Apr 2021 00:00:00 +0000

Trust-but-Verify in Cyber-Physical Systems

Kalyan Perumalla

PDF Cite Slides DOI

Trust-but-Verify in Cyber-Physical Systems

Wed, 28 Apr 2021 00:00:00 +0000

https://dl.acm.org/doi/abs/10.1145/3445969.3450434

Smart Semi-Supervised Accumulation of Large Repositories for Industrial Control Systems Device Information

Mon, 01 Feb 2021 00:00:00 +0000

R&D Needs for Digital Twins

Thu, 03 Dec 2020 00:00:00 +0000

Scale-Free Graph Networks with Trillions of Edges: Rapid Generation using 1000 GPUs

Sun, 01 Nov 2020 00:00:00 +0000

Next in Reversible Computing: Breaking the Memory-Computation Asymmetry

Mon, 05 Oct 2020 00:00:00 +0000

On the Robustness of Network Community Structure Under Addition of Edges

Tue, 01 Sep 2020 00:00:00 +0000

https://www.osti.gov/biblio/1661212

A Novel Vetting Approach to Cybersecurity Verification in Energy Grid Systems

Mon, 13 Jul 2020 00:00:00 +0000

https://www.osti.gov/servlets/purl/1661247

https://ieeexplore.ieee.org/abstract/document/9167562

Generating Massive Scale-free Networks: Novel Parallel Algorithms using the Preferential Attachment Model

Sat, 16 May 2020 00:00:00 +0000

https://dl.acm.org/doi/abs/10.1145/3391446

COVID-relevant Scalable Computational Research Directions and Tools

Thu, 09 Apr 2020 00:00:00 +0000

This seminar conveys some directions and technical ideas being pursued by the Discrete Computing Systems Group of the Computer Science and Mathematics Division, in collaboration with others at the lab. Some of the scalable computational tools ready for applying to challenging computational problems are presented. Actual working codes ready for customization to COVID-related efforts are described, which are built for scaling to supercomputing platforms such as Summit.

Topics that are covered include:

A high resolution simulator, ExaCorona, that scales from laptops to leadership class supercomputers, is outlined that uses a discrete event model of virus spread, with probabilistically timed state transitions at the individual level across millions of individuals represented with arbitray geography and mobility characteristics.
A clonable simulation framework, CloneX, is introduced that enables millions of “what-if” scenarios to be executed rapidly on thousands of GPUs of Summit and similar supercomputers. An SEIR-based epidemiological model is outlined for numerous what-if simulations of disease spread that can be executed for country-scale populations like India’s, with ‘what-if’ scenarios, each varying in the outbreak points (hotspots), quarantines, vaccinations and hospitalizations.
A machine learning pipeline for the prediction of material structure properties directly from their neutron scattering profiles (development as part of the ExaLearn ECP co-design project). A brief overview of this system is provided, which is being applied for studies of new therapeutic targets and viral protein-structure-assisted drug design studies related to the COVID outbreak.
Network science methods are outlined for detecting information cascades in time varying large-scale social communication networks. We discuss its implications for detecting occurrence/response or epidemic related events from Twitter and similar global interaction systems.

RL-HEMS: Reinforcement Learning-Based Home Energy Management System for HVAC Energy Optimization (OR-20-C051)

Tue, 04 Feb 2020 00:00:00 +0000

https://ashraem.confex.com/ashraem/w20/meetingapp.cgi/Paper/26352

A Digital Twin Framework for Testing, Evaluation and Deployment of Resilient Cyber-physical Systems

Wed, 01 Jan 2020 00:00:00 +0000

RL-HEMS: Reinforcement Learning Based Home Energy Management System for HVAC Energy Optimization.

Wed, 01 Jan 2020 00:00:00 +0000

Kensor: Coordinated Intelligence from Co-Located Sensors

Mon, 09 Dec 2019 00:00:00 +0000

https://ieeexplore.ieee.org/abstract/document/9006318

Energy Conservation Through Cloned Execution Of Simulations

Sun, 08 Dec 2019 00:00:00 +0000

https://ieeexplore.ieee.org/abstract/document/9004821

On the Effectiveness of Recurrent Neural Networks for Live Modeling of Cyber-Physical Systems

Mon, 11 Nov 2019 00:00:00 +0000

https://ieeexplore.ieee.org/abstract/document/9065023

Detecting Sensors and Inferring their Relations at Level-0 in Industrial Cyber-Physical Systems

Tue, 05 Nov 2019 00:00:00 +0000

https://ieeexplore.ieee.org/abstract/document/9032891

Volatile Memory Extraction-Based Approach for Level 0-1 CPS Forensics

Fri, 01 Nov 2019 00:00:00 +0000

https://www.osti.gov/biblio/1606814

Exact-Differential Simulation: Differential Processing of Large-Scale Discrete Event Simulations

Tue, 18 Jun 2019 00:00:00 +0000

https://dl.acm.org/doi/abs/10.1145/3301499

Towards Native Execution of Deep Learning on a Leadership-Class HPC System

Mon, 20 May 2019 00:00:00 +0000

https://ieeexplore.ieee.org/abstract/document/8778212

Novel Parallel Algorithms for Fast Multi-GPU-Based Generation of Massive Scale-Free Networks

Sat, 30 Mar 2019 00:00:00 +0000

https://link.springer.com/article/10.1007/s41019-019-0088-6

Slides

Tue, 05 Feb 2019 00:00:00 +0000

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Documentation

Normalcy, Magic, Miracle and Error: Emergence along a Reversibility Spectrum

Tue, 01 Jan 2019 00:00:00 +0000

https://www.osti.gov/biblio/1737650
Journal Name: Insights Journal (Durham University, Institute of Advanced Study)
Journal ID: ISSN 1756-2074

An Agent-based Model of the Observed Distribution of Wealth in the United States

Mon, 01 Oct 2018 00:00:00 +0000

https://link.springer.com/article/10.1007/s11403-017-0200-9

Efficient reversible uniform and non-uniform random number generation in UNU.RAN

Sun, 01 Apr 2018 00:00:00 +0000

https://www.osti.gov/biblio/1468255

Scalable Cloning on Large-Scale GPU Platforms with Application to Time-Stepped Simulations on Grids

Wed, 31 Jan 2018 00:00:00 +0000

https://dl.acm.org/doi/abs/10.1145/3158669

GPU-based parallel algorithm for generating massive scale-free networks using the preferential attachment model

Mon, 11 Dec 2017 00:00:00 +0000

https://ieeexplore.ieee.org/abstract/document/8258315

Concurrent conversation modeling and parallel simulation of the naming game in social networks

Sun, 03 Dec 2017 00:00:00 +0000

https://ieeexplore.ieee.org/abstract/document/8247853

Generating Billion-Edge Scale-Free Networks in Seconds: Performance Study of a Novel GPU-based Preferential Attachment Model

Sun, 01 Oct 2017 00:00:00 +0000

https://www.osti.gov/biblio/1399438

Design exploration of a Symmetric Pass Gate Adiabatic Logic for energy-efficient and secure hardware

Thu, 01 Jun 2017 00:00:00 +0000

https://www.sciencedirect.com/science/article/abs/pii/S016792601630044X

Efficient Simulation of Nested Hollow Sphere Intersections: for Dynamically Nested Compartmental Models in Cell Biology

Tue, 16 May 2017 00:00:00 +0000

https://dl.acm.org/doi/abs/10.1145/3064911.3064920

Computational Speed and Matching Quality using an Upper Bound on the Normalized Mutual Information

Mon, 01 May 2017 00:00:00 +0000

URL

Computing a Non-trivial Lower Bound on the Joint Entropy between Two Images

Wed, 01 Mar 2017 00:00:00 +0000

https://www.osti.gov/biblio/1347338

Performance of Point and Range Queries for In-memory Databases Using Radix Trees on GPUs

Mon, 12 Dec 2016 00:00:00 +0000

https://ieeexplore.ieee.org/abstract/document/7828553

Model-based Dynamic Control of Speculative Forays in Parallel Computation

Sun, 30 Oct 2016 00:00:00 +0000

https://www.sciencedirect.com/science/article/pii/S1571066116300706

Energy-Efficient and Secure S-Box Circuit Using Symmetric Pass Gate Adiabatic Logic

Mon, 11 Jul 2016 00:00:00 +0000

https://ieeexplore.ieee.org/abstract/document/7560215

Internal Project

Wed, 27 Apr 2016 00:00:00 +0000

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.

Nullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.

Cras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.

Suspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.

Aliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.

KP External Project

Wed, 27 Apr 2016 00:00:00 +0000

Model-based Dynamic Control of Speculative Forays in Parallel Computation

Fri, 01 Apr 2016 00:00:00 +0000

https://www.osti.gov/biblio/1408647

Identifying and Harnessing Concurrency for Parallel and Distributed Network Simulation

Wed, 10 Feb 2016 00:00:00 +0000

Andelfinger’s thesis online

On the Definability of Art

Fri, 27 Mar 2015 00:00:00 +0000

Research performed while authors were Fellows at the Institute of Advanced Study, Durham University, UK

Dr. Kalyan Perumalla – Oak Ridge National Laboratory
Prof. Richard Read – University of Western Australia

Efficient Parallel Discrete Event Simulation on Cloud/Virtual Machine Platforms

Thu, 01 Jan 2015 00:00:00 +0000

[Pub 149]

http://tomacs.acm.org/

Virtual Time-Aware Virtual Machine Systems

Tue, 01 Jul 2014 00:00:00 +0000

https://smartech.gatech.edu/bitstream/handle/1853/52321/YOGINATH-DISSERTATION-2014.pdf

Design of A High-Fidelity Testing Framework for Secure Electric Grid Control

Wed, 01 Jan 2014 00:00:00 +0000

[Pub 144]

http://wintersim.org

Discrete Event Execution with One-Sided and Two-Sided GVT Algorithms on 216,000 Processor Cores

Wed, 01 Jan 2014 00:00:00 +0000

[Pub 121]

http://tomacs.acm.org

Parallel Discrete Event Simulation

Wed, 01 Jan 2014 00:00:00 +0000

[Pub 147]

http://hpcs2014.cisedu.info/

Parallel Discrete Event Simulation using Supercomputers, Cloud/VM, and Accelerators

Wed, 01 Jan 2014 00:00:00 +0000

[Pub 150]

http://www.acm-sigsim-pads.org/

Reverse Computation for Rollback-based Fault Tolerance in Large Parallel Systems

Wed, 01 Jan 2014 00:00:00 +0000

[Pub 119]

http://link.springer.com/article/10.1007%2Fs10586-013-0277-4

Simulating Billion-Task Parallel Programs

Wed, 01 Jan 2014 00:00:00 +0000

[Pub 143]

http://atc.udg.edu/SPECTS2014/

Towards Reversible Basic Linear Algebra Subprograms: A Performance Study

Wed, 01 Jan 2014 00:00:00 +0000

[Pub 145]

http://www.springer.com/series/8183

Tutorial: Introduction to Reversible Computing

Wed, 01 Jan 2014 00:00:00 +0000

[Pub 146]

http://hpcs2014.cisedu.info/

Virtual Machine-Based Simulation Platform For MANET-Based Cyber Infrastructure

Wed, 01 Jan 2014 00:00:00 +0000

[Pub 148]

http://www.scs.org/jdms

Simulation des Réseaux à grande Échelle sur les architectures de calcules hètèrogénes

Sun, 01 Dec 2013 00:00:00 +0000

Romdhanne’s thesis online

Interfacing JavaScript and MPI

Tue, 13 Aug 2013 00:00:00 +0000

Reversibly Finding the Square Root of an Integer

Tue, 13 Aug 2013 00:00:00 +0000

Efficient Heterogeneous Execution on Large Multicore and Accelerator Platforms: Case Study Using a Block Tridiagonal Solver

Tue, 01 Jan 2013 00:00:00 +0000

[Pub 120]

http://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/

Empirical Evaluation of PDES Execution on Cloud and Virtual Machine Platforms

Tue, 01 Jan 2013 00:00:00 +0000

[Pub 135]

http://www.acm-sigsim-pads.org/

Improved Parallelization of the SIESTA Magneto-hydrodynamic Equilibrium Code Using Cyclic Reduction

Tue, 01 Jan 2013 00:00:00 +0000

[Pub 141]

http://www.aps.org/meetings/meeting.cfm?name=DPP13

Introduction to Reversible Computing

Tue, 01 Jan 2013 00:00:00 +0000

[Pub 122]

http://rcbook.org

Optimized Hypervisor Scheduler for Parallel Discrete Event Simulations on Virtual Machine Platforms

Tue, 01 Jan 2013 00:00:00 +0000

[Pub 134]

http://simutools.org/2013

Reversible Simulations of Elastic Collisions

Tue, 01 Jan 2013 00:00:00 +0000

[Pub 118]

http://dl.acm.org/citation.cfm?id=2457461

Reversible Simulations of Elastic Collisions (Extended arXiv version)

Tue, 01 Jan 2013 00:00:00 +0000

[Pub 123]

http://arxiv.org/abs/1302.1126v1

Revisiting Cyclic Reduction and Parallel Prefix-Based Algorithms for Tri-diagonal Systems of Equations

Tue, 01 Jan 2013 00:00:00 +0000

[Pub 117]

http://www.sciencedirect.com/science/article/pii/S0743731512002535

Discrete Event Execution and Reversibility: Challenges in the Path to Asynchrony for Massively Parallel Computing

Sun, 01 Jan 2012 00:00:00 +0000

[Pub 127]

http://www.siam.org/meetings/jmm12/

Discrete Event Modeling and Massively Parallel Execution of Epidemic Outbreak Phenomena

Sun, 01 Jan 2012 00:00:00 +0000

[Pub 115]

http://sim.sagepub.com/content/88/7/768

Parallel Simulation on Supercomputers

Sun, 01 Jan 2012 00:00:00 +0000

[Pub 125]

http://www.wintersim.org

ReveR-SES: Reversible Software Execution Systems

Sun, 01 Jan 2012 00:00:00 +0000

[Pub 137]

Runtime Performance And Virtual Network Control Alternatives In VM-Based High-Fidelity Network Simulations

Sun, 01 Jan 2012 00:00:00 +0000

[Pub 126]

http://www.wintersim.org

Scaling Optimization of the SIESTA MHD Code

Sun, 01 Jan 2012 00:00:00 +0000

[Pub 142]

http://www.aps.org/meetings/meeting.cfm?name=DPP12

Scaling the SIESTA Magnetohydrodynamics Equilibrium Code

Sun, 01 Jan 2012 00:00:00 +0000

[Pub 116]

http://onlinelibrary.wiley.com/doi/10.1002/cpe.2919/abstract

Taming Wild Horses: The Need for Virtual Time-based Scheduling of VMs in Network Simulations

Sun, 01 Jan 2012 00:00:00 +0000

[Pub 133]

http://mascots.cs.vt.edu

Towards Highly Interactive, GPU-based Evaluation of Evacuation Transport Scenarios at State-Scale

Sun, 01 Jan 2012 00:00:00 +0000

[Pub 111]

http://sim.sagepub.com/content/88/6/746

Efficiently Scheduling Multi-core Guest Virtual Machines on Multi-core Hosts in Network Simulation

Sat, 01 Jan 2011 00:00:00 +0000

[Pub 129]

http://sites.google.com/site/pads2011/

GVT Algorithms and Discrete Event Dynamics on 129K+ Processor Cores

Sat, 01 Jan 2011 00:00:00 +0000

[Pub 132]

http://www.hipc.org/hipc2011

Improved Parallelization of the SIESTA Magneto-hydrodynamic Equilibrium Code Using Cyclic Reduction

Sat, 01 Jan 2011 00:00:00 +0000

[Pub 128]

http://www.aps.org/meetings/meeting.cfm?name=DPP11

Improving Multi-Million Virtual Rank MPI Execution in MUPI

Sat, 01 Jan 2011 00:00:00 +0000

[Pub 131]

http://pdcc.ntu.edu.sg/mascots2011/

ReveR-SES: Reversible Software Execution Systems

Sat, 01 Jan 2011 00:00:00 +0000

[Pub 136]

http://science.energy.gov/ascr/ascac/meetings/nov-2011/

Reversible Parallel Discrete Event Formulation of a TLM-based Radio Signal Propagation Model

Sat, 01 Jan 2011 00:00:00 +0000

[Pub 110]

http://dl.acm.org/citation.cfm?id=2043639

Towards High Performance Discrete Event Simulations of Smart Electric Grids

Sat, 01 Jan 2011 00:00:00 +0000

[Pub 130]

µπ: A Scalable and Transparent System for Simulating MPI Programs

Fri, 01 Jan 2010 00:00:00 +0000

[Pub 102]

http://www.simutools.org

An Incremental Parallelization Approach Applied to the ORNL/NRC FAVOR Code

Fri, 01 Jan 2010 00:00:00 +0000

[Pub 124]

www.osti.gov

Bcyclic: A Parallel Block Tri-diagonal Matrix Cyclic Solver

Fri, 01 Jan 2010 00:00:00 +0000

[Pub 5]

http://www.sciencedirect.com/science/journal/00219991

Compiler-based Automation Approaches to Reverse Computation

Fri, 01 Jan 2010 00:00:00 +0000

[Pub 112]

http://www.pads-workshop.org/pads2010.html

Efficient Simulation of Agent-Based Models on Multi-GPU and Multi-Core Clusters

Fri, 01 Jan 2010 00:00:00 +0000

[Pub 109]

http://www.simutools.org

High-Performance Simulations for Capturing Feedback and Fidelity in Complex Networked Systems

Fri, 01 Jan 2010 00:00:00 +0000

[Pub 107]

http://www.siam.org/meetings/pp10/

On Deciding between Conservative and Optimistic Approaches on Massively Parallel Platforms

Fri, 01 Jan 2010 00:00:00 +0000

[Pub 114]

http://www.wintersim.org

Reversible Parallel Discrete-Event Execution of Large-scale Epidemic Outbreak Models

Fri, 01 Jan 2010 00:00:00 +0000

[Pub 108]

http://www.pads-workshop.org/pads2010.html

Supercomputing Applications of the Other Kind: Real-time Parallel Discrete Event Simulations of Large-scale, Smart Infrastructures

Fri, 01 Jan 2010 00:00:00 +0000

[Pub 113]

Towards Highly Interactive, GPU-based Evaluation of Evacuation Transport Scenarios at State-Scale

Fri, 01 Jan 2010 00:00:00 +0000

[Pub 106]

http://www.nationalevacuationconference.org

A Connectionist Modeling Approach to Rapid Analysis of Emergent Social Cognition Properties in Large-Populations

Thu, 01 Jan 2009 00:00:00 +0000

[Pub 2]

Computational Spectrum of Agent Model Simulation

Thu, 01 Jan 2009 00:00:00 +0000

[Pub 1]

Coping at the User-Level with Resource Limitations in the Cray Message Passing Toolkit MPI at Scale: How Not to Spend Your Summer Vacation

Thu, 01 Jan 2009 00:00:00 +0000

[Pub 25]

Cyber Security Experimentation: Gory Detail or None at All?

Thu, 01 Jan 2009 00:00:00 +0000

[Pub 101]

http://www.siam.org/meetings/an09/

GPU-based Real-Time Execution of Vehicular Mobility Models in Large-Scale Road Network Scenarios

Thu, 01 Jan 2009 00:00:00 +0000

[Pub 100]

Introduction to Simulations on GPUs

Thu, 01 Jan 2009 00:00:00 +0000

[Pub 80]

http://www.cs.unibo.it/ds-rt2009/

Perfect Reversal of Rejection Sampling Methods for First-Passage-Time and Similar Probability Distributions

Thu, 01 Jan 2009 00:00:00 +0000

[Pub 103]

http://www.osti.gov/bridge

Reversible Discrete Event Formulation and Optimistic Parallel Execution of Vehicular Traffic Models

Thu, 01 Jan 2009 00:00:00 +0000

[Pub 7]

Scalable Parallel Execution of an Event-based Radio Signal Propagation Model for Cluttered 3D Terrains

Thu, 01 Jan 2009 00:00:00 +0000

[Pub 22]

Scalable Parallel Execution of an Event-based Radio Signal Propagation Model for Cluttered 3D Terrains

Thu, 01 Jan 2009 00:00:00 +0000

[Pub 104]

http://www.osti.gov/bridge

Switching to High Gear: Opportunities for Grand-scale Real-time Parallel Simulations

Thu, 01 Jan 2009 00:00:00 +0000

[Pub 98]

http://www.cs.unibo.it/ds-rt2009/

Switching to High Gear: Opportunities for Grand-scale Real-time Parallel Simulations

Thu, 01 Jan 2009 00:00:00 +0000

[Pub 99]

Data Parallel Execution Challenges and Runtime Performance of Agent Simulations on GPUs

Tue, 01 Jan 2008 00:00:00 +0000

[Pub 27]

Efficient Execution on GPUs of Field-based Vehicular Mobility Models

Tue, 01 Jan 2008 00:00:00 +0000

[Pub 24]

Feasibility, Efficiency and Limits of Compiler-based Automation for Reversibility of Codes

Tue, 01 Jan 2008 00:00:00 +0000

[Pub 89]

High Performance Computing-based Experimentation for Cyber Infrastructure and Security

Tue, 01 Jan 2008 00:00:00 +0000

[Pub 88]

On the Reversibility of Newton-Raphson Root-Finding Method

Tue, 01 Jan 2008 00:00:00 +0000

[Pub 69]

Parallel Vehicular Traffic Simulations using Reverse Computation-based Optimistic Execution

Tue, 01 Jan 2008 00:00:00 +0000

[Pub 26]

A Case Study of Efficient Social Network Simulation through General Processing on Graphics Processing Units (GPGPUs)

Mon, 01 Jan 2007 00:00:00 +0000

[Pub 138]

http://science.energy.gov/wdts/

A Case Study of the Performance of Speculative Asynchronous Simulation on Parallel Computers

Mon, 01 Jan 2007 00:00:00 +0000

[Pub 139]

http://science.energy.gov/wdts/

A New Methodology for Multi-scale Simulation of Plasmas

Mon, 01 Jan 2007 00:00:00 +0000

[Pub 6]

A New Methodology for Multi-scale Simulation of Plasmas

Mon, 01 Jan 2007 00:00:00 +0000

[Pub 9]

An Analysis Approach to Large-Scale Vehicular Network Simulations

Mon, 01 Jan 2007 00:00:00 +0000

[Pub 28]

Application-Level Asynchronous Speculative Execution

Mon, 01 Jan 2007 00:00:00 +0000

[Pub 90]

Efficient Parallel Execution of Event-Driven Electromagnetic Hybrid Models

Mon, 01 Jan 2007 00:00:00 +0000

[Pub 8]

Handling Time Management under the HLA

Mon, 01 Jan 2007 00:00:00 +0000

[Pub 81]

Model Execution

Mon, 01 Jan 2007 00:00:00 +0000

[Pub 4]

Parallel and Distributed Simulation: Traditional Techniques and Recent Advances

Mon, 01 Jan 2007 00:00:00 +0000

[Pub 82]

Scaling Time Warp-based Discrete Event Execution to 10^4 Processors on a Blue Gene Supercomputer

Mon, 01 Jan 2007 00:00:00 +0000

[Pub 29]

Two Case Studies of Reversibility in Computational Methods

Mon, 01 Jan 2007 00:00:00 +0000

[Pub 140]

http://science.energy.gov/wdts/

A Systems Approach to Scalable Transportation Network Modeling

Sun, 01 Jan 2006 00:00:00 +0000

[Pub 30]

Discrete-Event Execution Alternatives on GPGPUs

Sun, 01 Jan 2006 00:00:00 +0000

[Pub 33]

Handling Time Management under the HLA

Sun, 01 Jan 2006 00:00:00 +0000

[Pub 84]

Integrated Analysis of Environment-Driven Operational Effects in Sensor Networks

Sun, 01 Jan 2006 00:00:00 +0000

[Pub 70]

Network Simulation

Sun, 01 Jan 2006 00:00:00 +0000

[Pub 3]

http://www.morganclaypool.com/toc/cnt/1/1

On Accounting for the Interplay of Kinetic and Non-Kinetic Aspects of Population Mobility Models

Sun, 01 Jan 2006 00:00:00 +0000

[Pub 31]

On Evaluation Needs of Real-life Sensor Network Deployments

Sun, 01 Jan 2006 00:00:00 +0000

[Pub 18]

Optimistic Simulation of Physical Systems using Reverse Computation

Sun, 01 Jan 2006 00:00:00 +0000

[Pub 10]

Parallel and Distributed Simulation: Traditional Techniques and Recent Advances

Sun, 01 Jan 2006 00:00:00 +0000

[Pub 17]

Parallel and Distributed Simulation: Traditional Techniques and Recent Advances

Sun, 01 Jan 2006 00:00:00 +0000

[Pub 83]

Parallel Execution of Region-Scale Evacuation Traffic Models

Sun, 01 Jan 2006 00:00:00 +0000

[Pub 32]

Scalable Simulation of Electromagnetic Hybrid Codes

Sun, 01 Jan 2006 00:00:00 +0000

[Pub 34]

µsik - A Micro-kernel for Parallel/Distributed Simulation Systems

Sat, 01 Jan 2005 00:00:00 +0000

[Pub 40]

A New Methodology for Multi-scale Simulation of Plasmas

Sat, 01 Jan 2005 00:00:00 +0000

[Pub 38]

A New Simulation Technique for Study of Collision-less Shocks: Self Adaptive Simulations

Sat, 01 Jan 2005 00:00:00 +0000

[Pub 37]

Computational Methods for Efficient Large-scale System Models

Sat, 01 Jan 2005 00:00:00 +0000

[Pub 92]

Computational Tools for Efficient Large-scale Discrete-Event Models

Sat, 01 Jan 2005 00:00:00 +0000

[Pub 91]

Distributed Simulation Systems & the High Level Architecture (HLA)

Sat, 01 Jan 2005 00:00:00 +0000

[Pub 85]

Optimistic Parallel Discrete Event Simulations of Physical Systems using Reverse Computation

Sat, 01 Jan 2005 00:00:00 +0000

[Pub 39]

Parallel and Distributed Systems and the High-Level Architecture

Sat, 01 Jan 2005 00:00:00 +0000

[Pub 19]

Parallel Discrete Event Simulations of Grid-based Models ? Asynchronous Electromagnetic Hybrid Code

Sat, 01 Jan 2005 00:00:00 +0000

[Pub 11]

Performance Prediction of Large-scale Parallel Discrete Event Models of Physical Systems

Sat, 01 Jan 2005 00:00:00 +0000

[Pub 35]

Virtual Simulator: An Infrastructure for Design and Performance-Prediction of Massively Parallel Codes

Sat, 01 Jan 2005 00:00:00 +0000

[Pub 105]

A Federated Approach to Distributed Network Simulation

Thu, 01 Jan 2004 00:00:00 +0000

[Pub 12]

A New Approach to Modeling Physical Systems: Discrete Event Simulations of Grid-based Models

Thu, 01 Jan 2004 00:00:00 +0000

[Pub 42]

Conservative Synchronization of Large-scale Network Simulations

Thu, 01 Jan 2004 00:00:00 +0000

[Pub 43]

Distributed Simulation Systems & the High Level Architecture (HLA)

Thu, 01 Jan 2004 00:00:00 +0000

[Pub 86]

High Fidelity Modeling of Computer Network Worms

Thu, 01 Jan 2004 00:00:00 +0000

[Pub 41]

Achieving Interoperability and Scalability in Simulation of Networks

Wed, 01 Jan 2003 00:00:00 +0000

[Pub 93]

Generating Perfect Reversals of Simple Linear Codes

Wed, 01 Jan 2003 00:00:00 +0000

[Pub 71]

Large-Scale Network Simulation - How Big? How Fast?

Wed, 01 Jan 2003 00:00:00 +0000

[Pub 44]

Power-aware State Dissemination in Mobile Distributed Virtual Environments

Wed, 01 Jan 2003 00:00:00 +0000

[Pub 45]

Scalable RTI-based Parallel Simulation of Networks

Wed, 01 Jan 2003 00:00:00 +0000

[Pub 46]

Techniques for Improving Accuracy and Usability in Large-scale Network Emulation

Wed, 01 Jan 2003 00:00:00 +0000

[Pub 72]

Experiences Applying Parallel and Interoperable Network Simulation Techniques in On-line Simulations of Military Networks

Tue, 01 Jan 2002 00:00:00 +0000

[Pub 48]

Updateable Simulations

Tue, 01 Jan 2002 00:00:00 +0000

[Pub 49]

Using Reverse Circuit Execution for Efficient Parallel Simulation of Logic Circuits

Tue, 01 Jan 2002 00:00:00 +0000

[Pub 47]

Web Services for Extensible Modeling and Simulation

Tue, 01 Jan 2002 00:00:00 +0000

[Pub 50]

Distributed Network Simulations using the Dynamic Simulation Backplane

Mon, 01 Jan 2001 00:00:00 +0000

[Pub 52]

Interactive Parallel Simulations with the JANE Framework

Mon, 01 Jan 2001 00:00:00 +0000

[Pub 13]

Virtual Time Synchronization over Unreliable Network Transport

Mon, 01 Jan 2001 00:00:00 +0000

[Pub 51]

An Approach to Federating Parallel Simulators

Sat, 01 Jan 2000 00:00:00 +0000

[Pub 54]

Design of High-performance RTI software

Sat, 01 Jan 2000 00:00:00 +0000

[Pub 53]

Parallel Simulation Backplanes for Mixed Signal Circuit Design

Sat, 01 Jan 2000 00:00:00 +0000

[Pub 73]

Using Reverse Computation towards Efficient Parallel/Distributed Computation

Sat, 01 Jan 2000 00:00:00 +0000

[Pub 94]

Using Reverse Computation towards Efficient Parallel/Distributed Computation

Sat, 01 Jan 2000 00:00:00 +0000

[Pub 95]

Efficient Optimistic Parallel Simulation using Reverse Computation

Fri, 01 Jan 1999 00:00:00 +0000

[Pub 56]

Efficient Optimistic Parallel Simulations using Reverse Computation

Fri, 01 Jan 1999 00:00:00 +0000

[Pub 14]

PARINO: A Parallel Branch and Cut Code

Fri, 01 Jan 1999 00:00:00 +0000

[Pub 57]

Source Code Transformations for Efficient Reversibility

Fri, 01 Jan 1999 00:00:00 +0000

[Pub 74]

The Effect of State Saving in Optimistic Simulation on a Cache-coherent Non-uniform Memory Access (CC-NUMA) Architecture

Fri, 01 Jan 1999 00:00:00 +0000

[Pub 55]

The High Level Architecture for Simulation

Fri, 01 Jan 1999 00:00:00 +0000

[Pub 87]

Efficient Large-scale Process-oriented Parallel Simulations

Thu, 01 Jan 1998 00:00:00 +0000

[Pub 58]

Parallel Simulation Techniques for Large-scale Networks

Thu, 01 Jan 1998 00:00:00 +0000

[Pub 15]

TeD - A Language for Modeling Telecommunication Networks

Thu, 01 Jan 1998 00:00:00 +0000

[Pub 21]

TeD Models for ATM Internetworks

Thu, 01 Jan 1998 00:00:00 +0000

[Pub 20]

Towards Reusable Modeling and Parallel Simulation of Telecommunication Networks

Thu, 01 Jan 1998 00:00:00 +0000

[Pub 96]

A Virtual PNNI Network Testbed

Wed, 01 Jan 1997 00:00:00 +0000

[Pub 60]

PARINO, A Parallel Integer Optimizer

Wed, 01 Jan 1997 00:00:00 +0000

[Pub 61]

PARINO: An Extensible Framework for Solving Mixed Integer Programs in Parallel

Wed, 01 Jan 1997 00:00:00 +0000

[Pub 75]

Reusable Modeling and Parallel Simulation of Networks using the TeD Language

Wed, 01 Jan 1997 00:00:00 +0000

[Pub 97]

Time Parallel Generation of Self-similar ATM Traffic

Wed, 01 Jan 1997 00:00:00 +0000

[Pub 59]

A C++ Instance of TeD

Mon, 01 Jan 1996 00:00:00 +0000

[Pub 78]

An Efficiency Prediction Method for ATM Multiplexer

Mon, 01 Jan 1996 00:00:00 +0000

[Pub 62]

GTW++ -- An Object Oriented Interface in C++ to the Georgia Tech Time Warp System

Mon, 01 Jan 1996 00:00:00 +0000

[Pub 76]

MetaTeD - A Meta Language for Modeling Telecommunication Networks

Mon, 01 Jan 1996 00:00:00 +0000

[Pub 77]

A Performance Prediction Method for ATM Multiplexers

Sun, 01 Jan 1995 00:00:00 +0000

[Pub 79]

Parallel Algorithms for Maximum Sub-sequence and Sub-array

Sun, 01 Jan 1995 00:00:00 +0000

[Pub 16]

Parallel Algorithms for Maximum Sub-sequence and Sub-array

Sat, 01 Jan 1994 00:00:00 +0000

[Pub 64]

Parallelizing Sequential Algorithms for the Generalized Assignment Problem

Sat, 01 Jan 1994 00:00:00 +0000

[Pub 63]

ftp://ftp.cc.gatech.edu/pub/tech_reports/1994/GIT-CC-94-44.ps.Z

A Debugging Environment for PVM

Fri, 01 Jan 1993 00:00:00 +0000

[Pub 66]

A Distributed Algorithm for Ear Decomposition

Fri, 01 Jan 1993 00:00:00 +0000

[Pub 65]

Integrating Aggregate and Vehicle Level Simulations

Fri, 01 Jan 1993 00:00:00 +0000

[Pub 67]

Integrating Battlefield Simulations of Different Granularity

Wed, 01 Jan 1992 00:00:00 +0000

[Pub 68]

Mon, 01 Jan 0001 00:00:00 +0000

Kalyan Perumalla is a Federal Program Manager for Computer Science in Advanced Scientific Computing Research (ASCR), Office of Science, within the U.S. Department of Energy (DOE), where he manages an R&D portfolio spanning AI, Quantum, HPC, SciDAC and SBIR programs.

Before joining DOE, he led advanced research and development (R&D) in high-end computing/supercomputing for 17 years in research and managerial roles up to Distinguished Research Staff Member at the Oak Ridge National Laboratory (ORNL), home to the world’s largest supercomputers. Prior to that, he held research faculty positions for eight years at the Georgia Institute of Technology.

His additional appointments included serving as Joint Full Professor in the School of Industrial and Systems Engineering at the University of Tennessee, Knoxville, as Adjunct Professor in the School of Computational Sciences and Engineering at the Georgia Institute of Technology, and as Adjunct Professor in the Department of Electrical and Computer Engineering at the University of Nebraska-Lincoln. He also served on the Special Interest Group Governing Board of the Association for Computing Machinery (ACM) as the elected chair for ACM Special Interest Group in Simulation (SIGSIM).

Among other engagements, he served as Fellow of the Institute of Advanced Study at Durham University, UK, and as member of the National Academies’ Technical Advisory Boards for the U.S. Army Research Laboratory.

Mon, 01 Jan 0001 00:00:00 +0000

Program Management Portfolio

Mon, 01 Jan 0001 00:00:00 +0000

Program Management Portfolio

Kalyan Perumalla

Career In Brief

DarkNet Cyber Resilience

Overview

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Selected Publications

CYVET

Overview

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Additional Background

Professional Biography

ASCR CS PI Meeting

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Frontiers in Big Data - Networks

Deep CYBERIA

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PADS'22 General Co-Chair

Exascale Computing for the National Energy Grid

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The Problem

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ReveR-SES: Reversible Software Execution Systems

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ACM SIGSIM Chair

ACM TOMACS

Journal of Simulation

NAERM

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Introduction to Reversible Computing

About

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SIMULATION: Transactions of TSMSI

Computational Epidemiology

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ZeroIn

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Graph Networks

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DES Grid

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AI ML Research

Overview