Marco Fornari | Kalyan Perumalla

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.

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
- …
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

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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

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.

Basic Research Needs in Quantum Computing and Networking

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

Agency Report

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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.