Abstract: In this paper, the effects of different task allocation strategies on the performance of sparse matrix and dense vector multiplication are discussed. It is observed that the selection of task allocation strategy can significantly affect the performance of sparse matrix, and there is no fixed task allocation strategy that can obtain the best performance for all sparse matrices. Therefore, this paper proposes an optimal task allocation strategy selection method based on machine learning. Its training process only uses sparse matrix features to characterize the input data set, and can automatically train the model for a given data set and target platform. Experiments show that, compared with the default block allocation method, the task allocation method selected by this model can achieve an average performance improvement of about 35%.

Key words: sparse matrix-vector multiplication, task allocation, machine learning