001     893756
005     20250822121434.0
024 7 _ |a 10.1145/3452412.3462752
|2 doi
024 7 _ |a 2128/28078
|2 Handle
024 7 _ |a WOS:001322551200001
|2 WOS
037 _ _ |a FZJ-2021-02811
041 _ _ |a English
100 1 _ |a Herten, Andreas
|0 P:(DE-Juel1)145478
|b 0
|e Corresponding author
|u fzj
111 2 _ |a The 30th International Symposium on High-Performance Parallel and Distributed Computing, PERMAVOST Workshop
|g HPDC21
|c Virtual
|d 2021-06-21 - 2021-06-25
|w Sweden
245 _ _ |a JUWELS Booster - Early User Experiences
260 _ _ |c 2021
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a Other
|2 DataCite
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a LECTURE_SPEECH
|2 ORCID
336 7 _ |a Conference Presentation
|b conf
|m conf
|0 PUB:(DE-HGF)6
|s 1625903664_29986
|2 PUB:(DE-HGF)
|x Plenary/Keynote
520 _ _ |a Over the last few years, GPUs became ubiquitous in HPC installations around the world. Today, they provide the main source of performance in a number of Top500 machines - for example Summit, Sierra, and JUWELS Booster. Also for the upcoming Exascale era, GPUs are selected as key enablers and will be installed numerously. While individual GPU devices already offer plenty of performance (O (10) TFLOP/s FP64), current and next-generation super-computers employ them in the thousands. Using these machines to the fullest extend means not only utilizing individual devices efficiently, but using the entire interconnected system of devices thoroughly.JUWELS Booster is a recently installed Tier-0/1 system at Jülich Supercomputing Centre (JSC), currently the 7th-fastest supercomputer in the world, and the fastest in Europe. JUWELS Booster features 936 nodes, each equipped with 4 NVIDIA A100 Tensor Core GPUs and 4 Mellanox HDR200 InfiniBand HCAs. The peak performance of all GPUs together sums up to 73 PFLOP/s and it features a DragonFly+ network topology with 800 Gbit/s network injection bandwidth per node.During installation of JUWELS Booster, a selected set of applications were given access to the system as part of the JUWELS Booster Early Access Program. To prepare for their first compute time allocation, scientific users were able to gain first experiences on the machine. They gave direct feedback to the system operations team during installation and beyond. Close collaboration was facilitated with the application support staff of JSC, giving unique insights into the individual processes of utilizing a brand-new large-sale system for a first time. Likewise, performance profiles of applications could be studied and collaboratively analyzed, employing available tools and methods. Performance limiters of the specific application on the platform were identified and proposals for improvement developed.This talk will present first experiences with JUWELS Booster and the applications utilizing the system during its first months. Applied methods for onboarding, analysis, and optimization will be shown and assessed. Highlights of the state of the art of performance analysis and modeling for GPUs will be presented with concrete examples from the JUWELS Booster Early Access Program.
536 _ _ |a 5121 - Supercomputing & Big Data Facilities (POF4-512)
|0 G:(DE-HGF)POF4-5121
|c POF4-512
|f POF IV
|x 0
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 1
536 _ _ |a ATML-X-DEV - ATML Accelerating Devices (ATML-X-DEV)
|0 G:(DE-Juel-1)ATML-X-DEV
|c ATML-X-DEV
|x 2
588 _ _ |a Dataset connected to CrossRef Conference
773 _ _ |a 10.1145/3452412.3462752
856 4 _ |u https://permavost.github.io/
856 4 _ |u https://juser.fz-juelich.de/record/893756/files/3452412.3462752.pdf
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/893756/files/Overlays-reduced%20Slides.pdf
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/893756/files/Slides.pdf
|y Restricted
909 C O |o oai:juser.fz-juelich.de:893756
|p openaire
|p open_access
|p VDB
|p driver
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)POF4-512
|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 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)POF4-511
|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 1
914 1 _ |y 2021
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a conf
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21