克服不规则稀疏矩阵的负载不平衡

Proceedings of the Seventh Workshop on Irregular Applications: Architectures and Algorithms Pub Date : 2017-11-12 DOI:10.1145/3149704.3149767

Goran Flegar, H. Anzt

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引用次数: 16

摘要

本文提出了一种计算稀疏矩阵向量积的负载均衡GPU内核。通过大量使用最新的GPU编程功能，我们还为不规则和不平衡矩阵提供了令人满意的性能。在使用400个测试矩阵的性能比较中，我们发现新内核优于最流行的SpMV实现。

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Overcoming Load Imbalance for Irregular Sparse Matrices

In this paper we propose a load-balanced GPU kernel for computing the sparse matrix vector (SpMV) product. Making heavy use of the latest GPU programming features, we also enable satisfying performance for irregular and unbalanced matrices. In a performance comparison using 400 test matrices we reveal the new kernel being superior to the most popular SpMV implementations.

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Proceedings of the Seventh Workshop on Irregular Applications: Architectures and Algorithms

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