GPU | Kalyan Perumalla

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).

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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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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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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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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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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

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

URL

Kalyan Perumalla, Maksudul Alam, Devin A White

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Mixed Integer Solver Technology for Accelerated Computing Systems (SBIR)

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

Solicitation PDF

Original at OSTI
Cached local copy

Selected Pages

Selected Extracts

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).

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.

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

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

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

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

Energy Conservation Through Cloned Execution Of Simulations

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

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

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

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

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

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

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

Identifying and Harnessing Concurrency for Parallel and Distributed Network Simulation

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

Andelfinger’s thesis online

Parallel Discrete Event Simulation

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

[Pub 147]

http://hpcs2014.cisedu.info/

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

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

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

Romdhanne’s thesis online

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/

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

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

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

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]

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

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

[Pub 27]

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/

Discrete-Event Execution Alternatives on GPGPUs

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

[Pub 33]

GPU | Kalyan Perumalla

ReveR-SES: Reversible Software Execution Systems

Overview

Scope

Primary Scientific Thrusts

Science Impacts

Gallery

Organization

Selected Publications

Digital Twin Framework

Overview

Organization

Gallery

Related Publications

EpiClone: Epidemiological Clonable Simulations

Overview

Gallery

Organization

Related Publications

CloneX

Overview

Organization

Gallery

Related Publications

BLOCKTRI: Parallel Block Tridiagonal Solver

Organization

Selected Publications

MutEnt

Overview

Organization

Related Publications

Gallery

Mixed Integer Solver Technology for Accelerated Computing Systems (SBIR)

Solicitation PDF

Selected Pages

Selected Extracts

ExaSGD Performance Profiling

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

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

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

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

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

Energy Conservation Through Cloned Execution Of Simulations

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

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

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

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

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

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

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

Identifying and Harnessing Concurrency for Parallel and Distributed Network Simulation

Parallel Discrete Event Simulation

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

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

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

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

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

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

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

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

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

Discrete-Event Execution Alternatives on GPGPUs