TY - JOUR

T1 - Analytical modeling of matrix–vector multiplication on multicore processors

AU - Gareev, Roman A.

AU - Akimova, Elena N.

PY - 2022/10/1

Y1 - 2022/10/1

N2 - The efficiency of matrix–vector multiplication is of considerable importance. No current approaches can optimize this sufficiently well under severe time constraints. All major existing methods are based on either manual-tuning or auto-tuning and can therefore be time-consuming. We introduce an alternative model-driven approach, which is used to map the implementation of matrix–vector multiplication to a target architecture and analytically obtain its parameters. The approach yields the performance that is competitive with optimized Basic Linear Algebra Subprograms (BLAS)-like dense linear algebra libraries without the need for manual-tuning or auto-tuning. Our method provides competitive performance across hardware architectures and can be utilized to obtain single-threaded and multi-threaded implementations on multicore processors. We expect that this approach allows the community to progress from valuable engineering solutions to techniques with a broader application. © 2021 John Wiley & Sons, Ltd.

AB - The efficiency of matrix–vector multiplication is of considerable importance. No current approaches can optimize this sufficiently well under severe time constraints. All major existing methods are based on either manual-tuning or auto-tuning and can therefore be time-consuming. We introduce an alternative model-driven approach, which is used to map the implementation of matrix–vector multiplication to a target architecture and analytically obtain its parameters. The approach yields the performance that is competitive with optimized Basic Linear Algebra Subprograms (BLAS)-like dense linear algebra libraries without the need for manual-tuning or auto-tuning. Our method provides competitive performance across hardware architectures and can be utilized to obtain single-threaded and multi-threaded implementations on multicore processors. We expect that this approach allows the community to progress from valuable engineering solutions to techniques with a broader application. © 2021 John Wiley & Sons, Ltd.

UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=000607457400001

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85099368206

U2 - 10.1002/mma.7045

DO - 10.1002/mma.7045

M3 - Article

VL - 45

SP - 8769

EP - 8799

JO - Mathematical Methods in the Applied Sciences

JF - Mathematical Methods in the Applied Sciences

SN - 0170-4214

IS - 15

ER -