% IMPORTANT: The following is UTF-8 encoded. This means that in the presence % of non-ASCII characters, it will not work with BibTeX 0.99 or older. % Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or % “biber”. @CONFERENCE{Herten:916420, author = {Herten, Andreas}, title = {{JUWELS} {B}ooster {T}uning and {S}caling {W}orkshop 2022}, reportid = {FZJ-2022-06220}, year = {2022}, note = {Indico: https://indico-jsc.fz-juelich.de/event/266/other-view?view=standard}, abstract = {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.}, month = {Mar}, date = {2022-03-07}, organization = {JUWELS Booster Tuning and Scaling Workshop 2022, online (Germany), 7 Mar 2022 - 11 Mar 2022}, cin = {JSC}, cid = {I:(DE-Juel1)JSC-20090406}, pnm = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) / 5122 - Future Computing $\&$ Big Data Systems (POF4-512) / 5121 - Supercomputing $\&$ Big Data Facilities (POF4-512) / ATML-X-DEV - ATML Accelerating Devices (ATML-X-DEV)}, pid = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-5122 / G:(DE-HGF)POF4-5121 / G:(DE-Juel-1)ATML-X-DEV}, typ = {PUB:(DE-HGF)5}, url = {https://juser.fz-juelich.de/record/916420}, }