DOE | Kalyan Perumalla

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

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

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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

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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

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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

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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

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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.

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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
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Quantum Devices and Sensors
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Continuation of Solicitation for the Office of Science Financial Assistance Program

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

Announcement

Press Release

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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.

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: …

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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

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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.

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).

Scientific Enablers of Quantum Communications

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

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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.