JUWELS Booster - A Supercomputer for Large-Scale AI Research

被引：3

作者：

Kesselheim, Stefan ^{[1
]}

Herten, Andreas ^{[1
]}

Krajsek, Kai ^{[1
]}

Ebert, Jan ^{[1
]}

Jitsev, Jenia ^{[1
]}

Cherti, Mehdi ^{[1
]}

Langguth, Michael ^{[1
]}

Gong, Bing ^{[1
]}

Stadtler, Scarlet ^{[1
]}

Mozaffari, Amirpasha ^{[1
]}

Cavallaro, Gabriele ^{[1
]}

Sedona, Rocco ^{[1
,2
]}

Schug, Alexander ^{[1
,3
]}

Strube, Alexandre ^{[1
]}

Kamath, Roshni ^{[1
]}

Schultz, Martin G. ^{[1
]}

Riedel, Morris ^{[1
,2
]}

Lippert, Thomas ^{[1
]}

机构：

[1] Forschungszentrum Julich, Julich Supercomp Ctr, Julich, Germany

[2] Univ Iceland, Sch Engn & Nat Sci, Reykjavik, Iceland

[3] Univ Duisburg Essen, Duisburg, Germany

来源：

HIGH PERFORMANCE COMPUTING - ISC HIGH PERFORMANCE DIGITAL 2021 INTERNATIONAL WORKSHOPS | 2021年 / 12761卷

基金：

欧洲研究理事会; 欧盟地平线“2020”;

关键词：

Artificial Intelligence; Deep learning; GPU; High-performance computing; Machine learning; Mich supercomputing centre; DIRECT-COUPLING ANALYSIS; RNA; IDENTIFICATION;

D O I：

10.1007/978-3-030-90539-2_31

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the Julich Supercomputing Center. With its system architecture, most importantly its large number of powerful Graphics Processing Units (GPUs) and its fast interconnect via InfiniBand, it is an ideal machine for large-scale Artificial Intelligence (AI) research and applications. We detail its system architecture, parallel, distributed model training, and benchmarks indicating its outstanding performance. We exemplify its potential for research application by presenting large-scale AI research highlights from various scientific fields that require such a facility.

引用

页码：453 / 468

页数：16

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