Towards Dynamic Autotuning of SpMV in CUSP Library

D 2024

Towards Dynamic Autotuning of SpMV in CUSP Library

DEMEK, Miroslav a Jiří FILIPOVIČ

Základní údaje

Originální název

Towards Dynamic Autotuning of SpMV in CUSP Library

Autoři

DEMEK, Miroslav (203 Česká republika, domácí) a Jiří FILIPOVIČ (203 Česká republika, garant, domácí)

Vydání

San Francisco, USA, IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), od s. 14-22, 9 s. 2024

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14610/24:00137324

Organizační jednotka

Ústav výpočetní techniky

ISBN

979-8-3503-6461-3

DOI

http://dx.doi.org/10.1109/IPDPSW63119.2024.00012

UT WoS

001284697300084

EID Scopus

2-s2.0-85200751879

Klíčová slova anglicky

dynamic autotuning; SpMV; CUPS; Kernel Tuning Toolkit

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 4. 4. 2025 13:12, Mgr. Eva Špillingová

Anotace

V originále

Sparse matrix-vector product (SpMV) is a central operation in many iterative methods for solving linear systems and, as such, is an attractive candidate for acceleration on the GPU. However, the performance of the SpMV kernel can vary depending both on the target architecture as well as on the sparsity pattern of the matrix. Thus, to achieve optimal performance, the implementation might need to be adjusted for each particular matrix and architecture. This can be achieved through dynamic autotuning, a method that can optimize a source code during program runtime. In this paper, we present a dynamic autotuning of SpMV kernel included in a production-quality CUSP library. We identify and implement tuning parameters and use the Kernel Tuning Toolkit framework for autotuning of SpMV working with the DIA and ELL sparse matrix formats. The dynamic autotuning integration is fully transparent to the library users - it can be activated just by re-compiling software using our tunable version of the CUSP. The proposed autotuned library is evaluated by comparing it with the original CUSP kernels on a set of representative matrices and by examining the contribution of autotuning. The results show that the autotuned kernels can reach up to about 16.9 × speedup compared to a fixed implementation.

Návaznosti

LM2023054, projekt VaV

Název: e-Infrastruktura CZ

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, e-Infrastruktura CZ

Citovat

DEMEK, Miroslav a Jiří FILIPOVIČ. Towards Dynamic Autotuning of SpMV in CUSP Library. Online. In IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). San Francisco, USA: IEEE, 2024, s. 14-22. ISBN 979-8-3503-6461-3. Dostupné z: https://dx.doi.org/10.1109/IPDPSW63119.2024.00012.

@inproceedings{2442222,
   author = {Demek, Miroslav and Filipovič, Jiří},
   address = {San Francisco, USA},
   booktitle = {IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
   doi = {http://dx.doi.org/10.1109/IPDPSW63119.2024.00012},
   keywords = {dynamic autotuning; SpMV; CUPS; Kernel Tuning Toolkit},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {San Francisco, USA},
   isbn = {979-8-3503-6461-3},
   pages = {14-22},
   publisher = {IEEE},
   title = {Towards Dynamic Autotuning of SpMV in CUSP Library},
   year = {2024}
}

TY  - CONF
ID  - 2442222
AU  - Demek, Miroslav - Filipovič, Jiří
PY  - 2024
TI  - Towards Dynamic Autotuning of SpMV in CUSP Library
PB  - IEEE
CY  - San Francisco, USA
SN  - 9798350364613
KW  - dynamic autotuning
KW  - SpMV
KW  - CUPS
KW  - Kernel Tuning Toolkit
N2  - Sparse matrix-vector product (SpMV) is a central operation in many iterative methods for solving linear systems and, as such, is an attractive candidate for acceleration on the GPU. However, the performance of the SpMV kernel can vary depending both on the target architecture as well as on the sparsity pattern of the matrix. Thus, to achieve optimal performance, the implementation might need to be adjusted for each particular matrix and architecture. This can be achieved through dynamic autotuning, a method that can optimize a source code during program runtime. In this paper, we present a dynamic autotuning of SpMV kernel included in a production-quality CUSP library. We identify and implement tuning parameters and use the Kernel Tuning Toolkit framework for autotuning of SpMV working with the DIA and ELL sparse matrix formats. The dynamic autotuning integration is fully transparent to the library users - it can be activated just by re-compiling software using our tunable version of the CUSP. The proposed autotuned library is evaluated by comparing it with the original CUSP kernels on a set of representative matrices and by examining the contribution of autotuning. The results show that the autotuned kernels can reach up to about 16.9 × speedup compared to a fixed implementation.
ER  -

DEMEK, Miroslav a Jiří FILIPOVIČ. Towards Dynamic Autotuning of SpMV in CUSP Library. Online. In \textit{IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)}. San Francisco, USA: IEEE, 2024, s.~14-22. ISBN~979-8-3503-6461-3. Dostupné z: https://dx.doi.org/10.1109/IPDPSW63119.2024.00012.

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