Memory Optimizations for Sparse Linear Algebra on GPU Hardware

An effort to maximize memory bandwidth utilization for a sparse linear algebra kernel executing on NVIDIA® Tesla V100 and A100 Graphics Processing Units (GPUs) is described. The kernel consists of a block-sparse matrix-vector product and a series of forward/backward triangular solves. The computatio...

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Bibliographische Detailangaben

Hauptverfasser:	Walden, Aaron, Zubair, Mohammad, Stone, Christopher P, Nielsen, Eric J
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Computer Programming And Software
Online-Zugang:	Volltext bestellen
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