TY  - CONF
AU  - Wu, Xinzhe
AU  - Di Napoli, Edoardo
TI  - Advancing the distributed Multi-GPU ChASE library through algorithm optimization and NCCL library
PB  - ACM New York, NY, USA
M1  - FZJ-2023-05321
SP  - 1688–1696
PY  - 2023
AB  - 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. Conversely, 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 modules, and substituting the MPI library by the vendor-optimized NCCL library. The resulting library can tackle dense problems with size up to , and scales effortlessly up to the full 900 nodes—each one powered by 4 × A100 NVIDIA GPUs—of the JUWELS Booster hosted at the Jülich Supercomputing Centre.
T2  - SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
CY  - 12 Nov 2023 - 17 Nov 2023, Denver, CO (USA)
Y2  - 12 Nov 2023 - 17 Nov 2023
M2  - Denver, CO, USA
LB  - PUB:(DE-HGF)8
DO  - DOI:10.1145/3624062.3624249
UR  - https://juser.fz-juelich.de/record/1019351
ER  -