A New Approach for Sparse Matrix Classification Based on Deep Learning Techniques

In this paper, a new methodology to select the best storage format for sparse matrices based on deep learning techniques is introduced. We focus on the selection of the proper format for the sparse matrix-vector multiplication (SpMV), which is one of the most important computational kernels in many scientific and engineering applications. Our approach considers the sparsity pattern of the matrices as an image, using the RGB channels to code several of the matrix properties. As a consequence, we generate image datasets that include enough information to successfully train a Convolutional Neural Network (CNN). Considering GPUs as target platforms, the trained CNN selects the best storage format 90.1% of the time, obtaining 99.4% of the highest SpMV performance among the tested formats.

keywords: Sparse matrix, Classification, Deep Learning, CNN, Performance