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Accelerating sparse matrix–matrix multiplication with GPU Tensor Cores

Published:2020-12 Issue: Volume:88 Page:106848
ISSN:0045-7906
Container-title:Computers & Electrical Engineering
language:en
Short-container-title:Computers & Electrical Engineering

Author:

Zachariadis Orestis,Satpute Nitin^ORCID,Gómez-Luna Juan^ORCID,Olivares Joaquín

Funder

European Union’s Horizon 2020 research and innovation programme

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,General Computer Science,Control and Systems Engineering

Reference30 articles.

1. Algorithm 9xx: SuiteSparse: Graphblas: graph algorithms in the language of sparse linear algebra;Davis,2018

2. High-performance graph algorithms from parallel sparse matrices;Gilbert,2006

3. Exposing fine-grained parallelism in algebraic multigrid methods;Bell;SIAM J Sci Comput,2012

4. On techniques to improve robustness and scalability of a parallel hybrid linear solver;Yamazaki,2010

5. Optimizing sparse matrix—Matrix multiplication for the GPU;Dalton;ACM Trans Math Software,2015

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1. Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs;Journal of Chemical Theory and Computation;2024-08-13

2. Bitmap-Based Sparse Matrix-Vector Multiplication with Tensor Cores;Proceedings of the 53rd International Conference on Parallel Processing;2024-08-12

3. Hacker group identification based on dynamic heterogeneous graph node update;Applied Soft Computing;2024-06

4. SPMSD: An Partitioning-Strategy for Parallel General Sparse Matrix-Matrix Multiplication on GPU;Parallel Processing Letters;2024-05-27

5. DTC-SpMM: Bridging the Gap in Accelerating General Sparse Matrix Multiplication with Tensor Cores;Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3;2024-04-27

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