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 similar to 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.

Multi GPU Sparse Matrix by Sparse Matrix Multiplication

Celestini A.;Bernaschi M.
2025

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 similar to 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.
2025
Istituto Applicazioni del Calcolo ''Mauro Picone''
CUDA
GPUs
large matrices
MPI
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/562541
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