More than 10 years of research related to the development of efficient GPU routines for the sparse matrix-vector product (SpMV) have led to several realizations, each with its own strengths and weaknesses. In this work, we review some of the most relevant efforts on the subject, evaluate a few prominent routines that are publicly available using more than 3000 matrices from different applications, and apply machine learning techniques to anticipate which SpMV realization will perform best for each sparse matrix on a given parallel platform. Our numerical experiments confirm the methods offer such varied behaviors depending on the matrix structure that the identification of general rules to select the optimal method for a given matrix becomes extremely difficult, though some useful strategies (heuristics) can be defined. Using a machine learning approach, we show that it is possible to obtain unexpensive classifiers that predict the best method for a given sparse matrix with over 80% accuracy, demonstrating that this approach can deliver important reductions in both execution time and energy consumption.

与针对稀疏矩阵矢量乘积（SpMV）的高效GPU例程的开发相关的10多年研究带来了多种实现，每种实现都有其优点和缺点。在这项工作中，我们回顾了与该主题最相关的一些工作，使用来自不同应用程序的3000多种矩阵，评估了一些公开的重要例程，并应用了机器学习技术来预测哪种SpMV实现对于每种稀疏情况都将表现最佳给定并行平台上的矩阵。我们的数值实验证实，根据矩阵结构的不同，这些方法提供的行为也多种多样，因此，尽管可以定义一些有用的策略（启发式方法），但要确定通用规则以选择给定矩阵的最佳方法却变得极为困难。