Quantum Computing | Kalyan Perumalla

Early Career Research Program (ECRP)

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

Solicitation PDF

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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.