001 | 916420 | ||
005 | 20250822121410.0 | ||
037 | _ | _ | |a FZJ-2022-06220 |
041 | _ | _ | |a English |
100 | 1 | _ | |a Herten, Andreas |0 P:(DE-Juel1)145478 |b 0 |e Corresponding author |u fzj |
111 | 2 | _ | |a JUWELS Booster Tuning and Scaling Workshop 2022 |c online |d 2022-03-07 - 2022-03-11 |w Germany |
245 | _ | _ | |a JUWELS Booster Tuning and Scaling Workshop 2022 |
260 | _ | _ | |c 2022 |
336 | 7 | _ | |a CONFERENCE |2 BibTeX |
336 | 7 | _ | |a conferenceObject |2 DRIVER |
336 | 7 | _ | |a Event |2 DataCite |
336 | 7 | _ | |a Conference / Event |b event |m event |0 PUB:(DE-HGF)5 |s 1671692845_17992 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a OTHER |2 ORCID |
336 | 7 | _ | |a Generic |0 31 |2 EndNote |
500 | _ | _ | |a Indico: https://indico-jsc.fz-juelich.de/event/266/other-view?view=standard |
520 | _ | _ | |a The Booster module of the JUWELS supercomputer at JSC consists of 936 compute nodes, each equipped with 4 NVIDIA A100 GPUs and providing a peak performance of over 70 PFLOP/s. Per node, 4 InfiniBand HDR200 adapters are installed, providing 800 GBit/s of network bandwidth per node. The system is in production since end of 2020 and used by many researchers for advanced simulations at large scales.This workshop will focus on applications already enabled for a few GPUs, with the goal to improve scaling and performance on many GPUs.Dedicated lectures will present state-of-the-art tools and techniques for efficient multi-GPU computing; while the main focus is on interactive hands-on sessions together with mentors, in which the applications of the participants will be analyzed and optimized. |
536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
536 | _ | _ | |a 5122 - Future Computing & Big Data Systems (POF4-512) |0 G:(DE-HGF)POF4-5122 |c POF4-512 |f POF IV |x 1 |
536 | _ | _ | |a 5121 - Supercomputing & Big Data Facilities (POF4-512) |0 G:(DE-HGF)POF4-5121 |c POF4-512 |f POF IV |x 2 |
536 | _ | _ | |a ATML-X-DEV - ATML Accelerating Devices (ATML-X-DEV) |0 G:(DE-Juel-1)ATML-X-DEV |c ATML-X-DEV |x 3 |
856 | 4 | _ | |u https://www.fz-juelich.de/en/ias/jsc/news/events/training-courses/2022/jwb-tuning-2022 |
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910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)145478 |
913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-512 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Supercomputing & Big Data Infrastructures |9 G:(DE-HGF)POF4-5122 |x 1 |
913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-512 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Supercomputing & Big Data Infrastructures |9 G:(DE-HGF)POF4-5121 |x 2 |
914 | 1 | _ | |y 2022 |
920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
980 | _ | _ | |a event |
980 | _ | _ | |a VDB |
980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
980 | _ | _ | |a UNRESTRICTED |
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