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Post-exascale supercomputing: research opportunities abound
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Zuo-ning Chen, Jack Dongarra, Zhi-wei Xu. Post-exascale supercomputing: research opportunities abound[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(10): 1203-1208.
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Abstract: Exascale supercomputing refers to the scientific research efforts and activities to build and use su-percomputers that can perform scientific computing at the speed of exaflops, or 1018 floating-point (64-bit) operations per second. Exascale supercomputing is a major milestone in surpassing the state-of-the-art standard of petascale supercomputing, i.e., 1015 floating-point operations per second, established a decade ago in 2008.
Exascale scientific computing research is already in full bloom worldwide. The USA leads this research and development direction, with federal government funding starting in as early as 2008. Japan, Europe, India, and China soon followed suit. The most recent focus on the field is in Europe, with 1.4 billion euros budgeted for building pre-exascale supercomputers by 2020, and an additional 2.7 billion euros proposed for building an exascale supercomputer by 2023 (Feldman, 2018). It is expected that multiple exascale supercomputers will become operational in the USA, Europe, and Asia by 2020–2024, supporting cutting edge research in many scientific fields.
In this context, the Chinese Academy of Engi-neering (CAE) organized a special issue of “Post- exascale Supercomputing” in Frontiers of Infor-mation Technology and Electronic Engineering, by inviting position papers from leading experts inside and outside China. This special issue targets 2020–2030 supercomputing systems that go beyond the existing exascale systems under construction. It focuses on innovative research ideas in systems ar-chitecture, processors, memory, storage, intercon-nects, operating systems, programming languages and compilers, and application frameworks. It foresees the convergence of high-performance computing (HPC) with big data computing, intelligence compu-ting (e.g., deep learning), cloud computing, and edge computing, and encourages consideration of future HPC workloads.
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