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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >A Pattern-Based SpGEMM Library for Multi-Core and Many-Core Architectures
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A Pattern-Based SpGEMM Library for Multi-Core and Many-Core Architectures

机译：用于多核和多核架构的基于模式的SPGEMM库

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General sparse matrix-matrix multiplication (SpGEMM) is one of the most important mathematical library routines in a number of applications. In recent years, several efficient SpGEMM algorithms have been proposed, however, most of them are based on the compressed sparse row (CSR) format, and the possible performance gain from exploiting other formats has not been well studied. And some specific algorithms are restricted to parameter tuning that has a significant impact on performance. So the particular format, algorithm, and parameter that yield the best performance for SpGEMM remain undetermined. In this article, we conduct a prospective study on format-specific parallel SpGEMM algorithms and analyze their pros and cons. We then propose a pattern-based SpGEMM library, that provides a unified programming interface in the CSR format, analyses the pattern of two input matrices, and automatically determines the best format, algorithm, and parameter for arbitrary matrix pairs. For this purpose, we build an algorithm set that integrates three new designed algorithms with existing popular libraries, and design a hybrid deep learning model called MatNet to quickly identify patterns of input matrices and accurately predict the best solution by using sparse features and density representations. The evaluation shows that this library consistently outperforms the state-of-the-art library. We also demonstrate its adaptability in an AMG solver and a BFS algorithm with 30 percent performance improvement.
机译：一般稀疏矩阵矩阵乘法（SPGEMM）是许多应用中最重要的数学库例程之一。近年来，已经提出了几种有效的SPGEMM算法，然而，它们中的大多数基于压缩稀疏行（CSR）格式，并且从利用其他格式的可能性能增益也没有得到很好地研究。一些特定算法仅限于参数调整，对性能产生重大影响。因此，产生SPGEMM最佳性能的特定格式，算法和参数仍未确定。在本文中，我们对特定格式的并行SPGEMM算法进行了前瞻性研究，并分析了他们的利弊。然后，我们提出了一个基于模式的SPGEMM库，它在CSR格式提供统一的编程接口，分析了两个输入矩阵的模式，并自动确定任意矩阵对的最佳格式，算法和参数。为此目的，我们构建一个算法集，它将三个新设计的算法与现有的流行库集成，并设计一个名为MATNet的混合深度学习模型，以快速识别输入矩阵的模式，并通过使用稀疏特征和密度表示准确地预测最佳解决方案。评估表明，该图书馆始终如一地优于最先进的图书馆。我们还展示了AMG求解器和BFS算法的适应性，性能改进30％。

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