Naslov
ChASE: a distributed hybrid CPU-GPU eigensolver for large-scale hermitian eigenvalue problems

Autori
Wu, Xinzhe ; Davidović, Davor ; Achilles, Sebastian ; Di Napoli, Edoardo

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
PASC'22: Proceedings of the Platform for Advanced Scientific Computing Conference / - New York (NY) : The Association for Computing Machinery (ACM), 2022

ISBN
978-1-4503-9410-9

Skup
Platform for Advanced Scientific Computing Conference (PASC)

Mjesto i datum
Basel, Švicarska, 27.06.2022. - 29.06.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Subspace iteration eigensolve ; Dense Hermitian matrix ; Chebyshev polynomial ; Distributed hybrid CPU-GPU ; Heterogeneous GPU supercomputers

Sažetak
As modern massively parallel clusters are getting larger with beefier compute nodes, traditional parallel eigensolvers, such as direct solvers, struggle keeping the pace with the hardware evolution and being able to scale efficiently due to additional layers of communication and synchronization. This difficulty is especially important when porting traditional libraries to heterogeneous computing architectures equipped with accelerators, such as Graphics Processing Unit (GPU). Recently, there have been significant scientific contributions to the development of filter-based subspace eigensolver to compute partial eigenspectrum. The simpler structure of these type of algorithms makes for them easier to avoid the communica tion and synchronization bottlenecks typical of direct solvers. The Chebyshev Accelerated Subspace Eigensolver (ChASE) is a modern subspace eigensolver to compute partial extremal eigenpairs of large-scale Hermitian eigenproblems with the acceleration of a filter based on Chebyshev polynomials. In this work, we extend our previous work on ChASE by adding support for distributed hybrid CPU-multi-GPU computing architectures. Out tests show that ChASE achieves very good scaling performance up to 144 nodes with 526 NVIDIA A100 GPUs in total on dense eigenproblems of size up to 360k.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Računarstvo

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