Abstract
The supercomputer “Fugaku” is an exascale manycore-based parallel system developed as a Japanese national flagship supercomputer in the FLAGSHIP 2020 Project. While “Fugaku” was ranked first for several benchmarks such as TOP500, HPCG, HPL-AI, and Graph500 in 2020, the major design concept is the application-first concept by the co-design for power efficiency and high performance. We have designed an original manycore processor based on Armv8 instruction sets with the scalable vector extension, A64FX processor, with Fujitsu, our industry partner. The system consists of 158,976 nodes with 7.6 million cores in total and a theoretical peak of 537 Peta Floating-point Operations Per Second in double-precision floating points, connected by Tofu-D interconnect. The high performance computing (HPC)-oriented design enables an extremely good performance for memory-intensive workloads thanks to HBM2 memory with breakthrough power efficiency. In this article, we present the pragmatic practice of our co-design effort for “Fugaku” followed by an overview and the performance of “Fugaku.”
Highlights
A challenge for exascale computing is to develop a system capable of exascale computing and operable within an affordable power budget
We have carried out the FLAGSHIP 2020 Project [2] to develop the Japanese flagship supercomputer system following the K computer from 2014 to 2020
To decide the cache structure and size, we examined the impact of the cache configuration on the performance by running some kernels extracted from target applications on the simulator for a single Core-Memory Group (CMG)
Summary
A challenge for exascale computing is to develop a system capable of exascale computing and operable within an affordable power budget. Co-design has been proposed as a methodology for scientific application, software, and hardware communities to work together for designing future HPC systems. We have carried out the FLAGSHIP 2020 Project [2] to develop the Japanese flagship supercomputer system following the K computer from 2014 to 2020. At the beginning of the project, we defined the following three KPIs (Key Performance Indicators) as design targets. Since increasing power consumption is a critical issue in the design of the exascale large-scale supercomputer, it is important for the co-design to make trade-offs between energy/power, cost, and performance by taking application characteristics into consideration. In the co-design process, a set of target applications was provided from each area of the nine priority issues
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