多GPU稀疏矩阵的稀疏矩阵乘法

IF 1.5 4区计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Concurrency and Computation-Practice & Experience Pub Date : 2025-09-30 DOI:10.1002/cpe.70313

Artem Mavliutov, Giovanni Isotton, Carlo Janna, Alessandro Celestini, Massimo Bernaschi

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引用次数: 0

摘要

本文重点对现有的nsparse Nagasaka等算法进行改进，并将其扩展到多gpu设置，以便于实际工程问题的应用。在这项工作中，我们为SpGEMM提出了一个分布式多gpu框架，该框架是专门为n稀疏算法设计的。结果表明，nsparse的速度提高了2倍，并且随着gpu数量的增加，多gpu扩展接近理想的可扩展性。最后，我们通过计算双SpGEMM积来验证该算法在AMG设置下的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Multi GPU Sparse Matrix by Sparse Matrix Multiplication

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Multi GPU Sparse Matrix by Sparse Matrix Multiplication

The paper focuses on the improvement of the existing nsparse Nagasaka et al. algorithm and its extension to the multi-GPU setting for the application of real engineering problems. In this work, we propose a distributed multi-GPU framework for SpGEMM that is designed specifically for the nsparse like algorithms. The results show ∼2 times speed-up for nsparse and close to ideal scalability of the multi-GPU extension with the number of GPUs. Finally, we test the proposed algorithm in the AMG setting by computing the double SpGEMM product.

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来源期刊

Concurrency and Computation-Practice & Experience 工程技术-计算机：理论方法

CiteScore

5.00

自引率

10.00%

发文量

664

审稿时长

9.6 months

期刊介绍： Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.