Work

Multi GPU Sparse Matrix by Sparse Matrix Multiplication

Year: 2025

Type: article

Abstract: ABSTRACT 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.

Source: Concurrency and Computation Practice and Experience

DOI: 10.1002/cpe.70313

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

Institutions: University of Padua, National Research Council

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $5,140

OpenAlex ID: W4414622721

FWCI:

Cites: 15

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