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GPU-accelerated co-design of induced dimension reduction: algorithmic fusion and kernel overlap

Authors:

Gregory D. Peterson,

Jack DongarraAuthors Info & Claims

Co-HPC '15: Proceedings of the 2nd International Workshop on Hardware-Software Co-Design for High Performance Computing

Article No.: 5, Pages 1 - 8

https://doi.org/10.1145/2834899.2834907

Published: 15 November 2015 Publication History

Abstract

In this paper we present an optimized GPU co-design of the Induced Dimension Reduction (IDR) algorithm for solving linear systems. Starting from a baseline implementation based on the generic BLAS routines from the MAGMA software library, we apply optimizations that are based on kernel fusion and kernel overlap. Runtime experiments are used to investigate the benefit of the distinct optimization techniques for different variants of the IDR algorithm. A comparison to the reference implementation reveals that the interplay between them can succeed in cutting the overall runtime by up to about one third.

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Cited By

Hijma PHeldens SSclocco Avan Werkhoven BBal H(2023)Optimization Techniques for GPU ProgrammingACM Computing Surveys10.1145/357063855:11(1-81)Online publication date: 16-Mar-2023
https://dl.acm.org/doi/10.1145/3570638
Anzt HKreutzer MPonce EPeterson GWellein GDongarra J(2018)Optimization and performance evaluation of the IDR iterative Krylov solver on GPUsInternational Journal of High Performance Computing Applications10.1177/109434201664684432:2(220-230)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1177/1094342016646844
Anzt HDongarra JKreutzer MWellein GKohler M(2016)Efficiency of General Krylov Methods on GPUs -- An Experimental Study2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW.2016.45(683-691)Online publication date: May-2016
https://doi.org/10.1109/IPDPSW.2016.45

Index Terms

GPU-accelerated co-design of induced dimension reduction: algorithmic fusion and kernel overlap
1. Mathematics of computing
  1. Mathematical software
    1. Solvers

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cover image ACM Conferences

Co-HPC '15: Proceedings of the 2nd International Workshop on Hardware-Software Co-Design for High Performance Computing

November 2015

61 pages

ISBN:9781450339926

DOI:10.1145/2834899

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGHPC: ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing
SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS\DATC: IEEE Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2015

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U.S. Department of Energy

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SC15

Sponsor:

SIGHPC
SIGARCH
IEEE-CS\DATC

SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis

November 15, 2015

Texas, Austin

Acceptance Rates

Co-HPC '15 Paper Acceptance Rate 7 of 13 submissions, 54%;

Overall Acceptance Rate 7 of 13 submissions, 54%

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Cited By

Hijma PHeldens SSclocco Avan Werkhoven BBal H(2023)Optimization Techniques for GPU ProgrammingACM Computing Surveys10.1145/357063855:11(1-81)Online publication date: 16-Mar-2023
https://dl.acm.org/doi/10.1145/3570638
Anzt HKreutzer MPonce EPeterson GWellein GDongarra J(2018)Optimization and performance evaluation of the IDR iterative Krylov solver on GPUsInternational Journal of High Performance Computing Applications10.1177/109434201664684432:2(220-230)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1177/1094342016646844
Anzt HDongarra JKreutzer MWellein GKohler M(2016)Efficiency of General Krylov Methods on GPUs -- An Experimental Study2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW.2016.45(683-691)Online publication date: May-2016
https://doi.org/10.1109/IPDPSW.2016.45

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