WISE: Predicting the Performance of Sparse Matrix Vector Multiplication with Machine Learning
WISE: Predicting the Performance of Sparse Matrix Vector Multiplication with Machine Learning
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DOI：
10.1145/3572848.3577506
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Serif Yesil;Azin Heidarshenas;Adam Morrison;J. Torrellas
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文献类型：
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作者：
Serif Yesil;Azin Heidarshenas;Adam Morrison;J. Torrellas
稀疏的矩阵乘积（SPMV）是必不可少的稀疏内核。为了预测给定稀疏矩阵的最佳SPMV方法。我们开发了一个名为Wise的机器学习框架，该框架准确地预测了给定的稀疏矩阵的基线方法的不同SPMV方法的幅度然后可以为每个特定矩阵选择最佳的SPMV方法。 24核服务器中的英特尔MKL。
Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse kernel. Numerous methods have been developed to accelerate SpMV. However, no single method consistently gives the highest performance across a wide range of matrices. For this reason, a performance prediction model is needed to predict the best SpMV method for a given sparse matrix. Unfortunately, predicting SpMV's performance is challenging due to the diversity of factors that impact it. In this work, we develop a machine learning framework called WISE that accurately predicts the magnitude of the speedups of different SpMV methods over a baseline method for a given sparse matrix. WISE relies on a novel feature set that summarizes a matrix's size, skew, and locality traits. WISE can then select the best SpMV method for each specific matrix. With a set of nearly 1,500 matrices, we show that using WISE delivers an average speedup of 2.4× over using Intel's MKL in a 24-core server.