Joint direct and transposed sparse matrix-vector multiplication for multithreaded CPUs

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00346816" target="_blank" >RIV/68407700:21240/21:00346816 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1002/cpe.6236" target="_blank" >https://doi.org/10.1002/cpe.6236</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/cpe.6236" target="_blank" >10.1002/cpe.6236</a>

Result language

angličtina

Original language name

Joint direct and transposed sparse matrix-vector multiplication for multithreaded CPUs

Original language description

Repeatedly performing sparse matrix-vector multiplication (SpMV) followed by transposed sparse matrix-vector multiplication (SpMᵀV) with the same matrix is a part of several algorithms, for example, the Lanczos biorthogonalization algorithm and the biconjugate gradient method. Such algorithms can benefit from combining parallel SpMV and SpMᵀV into a single operation we call `joint direct and transposed sparse matrix-vector multiplication’ (SpMMᵀV). In this article, we present a parallel SpMMᵀV algorithm for shared-memory CPUs. The algorithm uses a sparse matrix format that divides the stored matrix into sparse matrix blocks and compresses the row and column indices of the matrix. This sparse matrix format can be also used for SpMV, SpMᵀV, and similar sparse matrix-vector operations. We expand upon existing research by suggesting new variants of the parallel SpMMᵀV algorithm and by extending the algorithm to efficiently support symmetric matrices. We compare the performance of the presented parallel SpMMᵀV algorithm with alternative approaches, which use state-of-the-art sparse matrix formats and libraries, using sparse matrices from real-world applications. The performance results indicate that the median performance of our proposed parallel SpMMᵀV algorithm is up to 45% higher than of the alternative approaches.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Concurrency and Computation: Practice and Experience

ISSN

1532-0626

e-ISSN

1532-0634

Volume of the periodical

33

Issue of the periodical within the volume

13

Country of publishing house

US - UNITED STATES

Number of pages

26

Pages from-to

1-26

UT code for WoS article

000620329400001

EID of the result in the Scopus database

2-s2.0-85101241296