Preprint

FZJ-2023-05135

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png tfQMRgpu: A GPU-accelerated linear solver with block-sparse complex result matrix

Baumeister, P. F. (Corresponding author)FZJ* ; Nassyr, S.FZJ*

2023
Springer Science + Business Media B.V Dordrecht [u.a.]

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Please use a persistent id in citations: doi:10.21203/rs.3.rs-3574519/v1

Abstract: Linear solvers are a central component of many applications in physics and engineering. In this work we present a software package for simultaneously solving with multiple right-hand sides using the vast compute performance and memory bandwidth of graphical processors. Using the transpose-free quasi minimal residual method iterative linear solving does not require the implementation of an adjoint operator. This C++/CUDA software packet has two ways of being employed. The precompiled version of this library offers linear solving for single and double precision block-sparse complex matrices with interfaces to various programming languages, in particular C, Fortran, Python and Julia. Furthermore, the core algorithm is available for custom implementations of any linear operator as a C++ header-only library. We showcase a matrix-free approach of a custom operator for a finite-difference stencil application solving the three-dimensional Helmholtz equation and compare the performance of the matrix-free approach against the block-sparse matrix version, both on NVIDIA hardware.

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Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2023

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; DEAL Springer ; DEAL Springer ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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