Targeting exa-scale systems; Performance portability and scalable data analysis

Asahi, Yuichi ; Maeyama, Shinya*; Bigot, J.*; Garbet, X.*; Grandgirard, V.*; Obrejan, K.*; Padioleau, T.*; Fujii, Keisuke*; Shimokawabe, Takashi*; Watanabe, Tomohiko*; Idomura, Yasuhiro ; Onodera, Naoyuki ; Hasegawa, Yuta ; Aoki, Takayuki*

We will demonstrate the performance portable implementation of a kinetic plasma code over CPUs, Nvidia and AMD GPUs. We will also discuss the performance portability of the code with C++ parallel algorithm. Deep learning based surrogate models for fluid simulations will also be demonstrated.

Language	:	Japanese
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Meeting title	:	JHPCN: 学際大規模情報基盤共同利用・共同研究拠点第14回シンポジウム
Held date	:	2022/07
Location (city)	:	東京
Location (country)	:	日本
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Keywords	:	Deep learning; GPU computing
Research Facility	:	大型計算機・スパコン(東海)
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Registration No. : BB20220562
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