I'm kind of betting that the static 32-wide ISAs of GPUs will win over in the long run, surely 32-wide (1024-bits wide) is wide enough, and there are multiple instructions (permute / bpermute) which are innately tied to the width of the SIMD-processor. And Nvidia is similarly 32-wide at the ISA level. In contrast, AMD has a big 64-wide to 32-wide change from GCN Vega -> RDNA. So future ARM-SVE implementations may increase (or decrease) the vector width without any need for recompiling. Its allegedly easier to auto-vectorize compared to AVX2 or AVX512, and the SVE instruction set is unique in that the ISA itself is independent of the SIMD-width. I hope to hear good things from those who work with the ARM-SVE instruction set. SIMD-compute in supercomputers is pretty commonplace these days, but every implementation brings forth new ideas and optimizations.
#Intel linpack benchmark v11.3.0.004 Pc#
*Fujitsu, the University of Tokyo, and Tohoku University have jointly developed an AI model, which can be performed on a regular PC in seconds, with the ability to predict tsunami flooding in coastal areas in near real-time with the computational power of Fugaku.The cool part about this supercomputer is that this is the first major implementation of the ARM-SVE (Scalable Vector Extension) instruction set. These are benchmarks generated with the benchFoam-script which is part of the PyFoam-distribution version 0.2.4.To compare with the results on this page use the config file standardv1.cfg (this is not going to change anymore, standard.cfg is an ongoing effort and will eventually become standardv2. We would like to once again express our sincere gratitude to RIKEN and others for their great cooperation and support. Fujitsu has been researching and applying high-performance computing, including the realization of real-time tsunami prediction using Fugaku (*) and will continue to promote this research to contribute to the realization of Society 5.0. We hope that many researchers will continue to leverage Fugaku’s world-leading performance and will contribute to the development of science and technology and the realization of a safe and secure society. Naoki Shinjo, Corporate Executive Officer, Deputy Head of Future Society & Technology Unit at Fujitsu Limited, stated " We are thrilled that we were able to successfully claim the top spot on the major benchmarks for the fourth consecutive term. RIKEN will continue to advance the capability of Fugaku and utilize its power through the newly established “Office of Fugaku Society 5.0 Initiative” to achieve Japan's Society 5.0/SDGs and a decarbonized society.
Now, Fugaku has once again proven most powerful in a wide range of areas. It has helped produce results that are useful in the real world, bringing about a so-called DX through its high performance in various fields. For the fourth time in its history, Fugaku not only became the overwhelming world leader in key benchmarks for simulation, big data and AI, but has made great contributions to the formulation of infection guidelines for the government and companies to combat COVID-19. It earned a score of 102,955 gigaTEPS.Īccording to Satoshi Matsuoka, director of RIKEN R-CCS, “Fugaku has been researched and developed as an embodiment of our country's world's best IT technology, which combines performance, power savings and ease of programming. The top ranking on Graph 500 was won by a collaboration involving RIKEN, Kyushu University, Fixstars Corporation, and Fujitsu.
On HPCG, it scored 16.00 petaflops, and on HPL-AI it gained a score of 2.004 exaflops.
On the Top500, it achieved a LINPACK score of 442.01 petaflops.
#Intel linpack benchmark v11.3.0.004 full#
The results this time were made with Fugaku's full complement of 158,976 nodes fit into 432 racks.
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