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| Hauptseite > Publikationsdatenbank > Advancing the Distributed Multi-GPU ChASE Library through Algorithm Optimization and NCCL Library > print |
| Hauptseite > Publikationsdatenbank > Advancing the Distributed Multi-GPU ChASE Library through Algorithm Optimization and NCCL Library > print |
| 001 | 1019352 | ||
| 005 | 20231220201928.0 | ||
| 037 | _ | _ | |a FZJ-2023-05322 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Di Napoli, Edoardo |0 P:(DE-Juel1)144723 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 14th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems |g ScalAH23 |c Denver |d 2023-11-12 - 2023-11-17 |w USA |
| 245 | _ | _ | |a Advancing the Distributed Multi-GPU ChASE Library through Algorithm Optimization and NCCL Library |
| 260 | _ | _ | |c 2023 |
| 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 1703049070_6181 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a As supercomputers become larger with powerful Graphics Processing Unit (GPU), traditional direct eigensolvers struggle to keep up with the hardware evolution and scale efficiently due to communication and synchronization demands. Subspace eigensolvers, like the Chebyshev Accelerated Subspace Eigensolver (ChASE), have a simpler structure and can overcome communication and synchronization bottlenecks. ChASE is a modern subspace eigensolver that uses Chebyshev polynomials to accelerate the computation of extremal eigenpairs of dense Hermitian eigenproblems. In this work we show how we have modified ChASE by rethinking its memory layout, introducing a novel parallelization scheme, switching to a more performing communication-avoiding algorithm for one of its inner module, and substituting MPI library by vendor-optimized NCCL library. The resulting library can tackle dense problems with size up to N=O(10^6), and scales effortlessly up to the full 900 nodes---each one powered by 4xA100 NVIDIA GPUs---of the JUWELS Booster hosted at the Jülich Supercomputing Centre. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a Simulation and Data Laboratory Quantum Materials (SDLQM) (SDLQM) |0 G:(DE-Juel1)SDLQM |c SDLQM |f Simulation and Data Laboratory Quantum Materials (SDLQM) |x 1 |
| 700 | 1 | _ | |a Wu, Xinzhe |0 P:(DE-Juel1)178969 |b 1 |e Corresponding author |u fzj |
| 856 | 4 | _ | |u https://sc23.conference-program.com/presentation/?id=ws_scalah103&sess=sess445 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1019352 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)144723 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)178969 |
| 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)POF4-5111 |x 0 |
| 914 | 1 | _ | |y 2023 |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)CASA-20230315 |k CASA |l Center for Advanced Simulation and Analytics |x 1 |
| 980 | _ | _ | |a conf |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)CASA-20230315 |
| 980 | _ | _ | |a UNRESTRICTED |
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