A lightweight optimization selection method for Sparse Matrix-Vector Multiplication

In this paper, we propose an optimization selection methodology for the ubiquitous sparse matrix-vector multiplication (SpMV) kernel. We propose two models that attempt to identify the major performance bottleneck of the kernel for every instance of the problem and then select an appropriate optimization to tackle it. Our first model requires online profiling of the input matrix in order to detect its most prevailing performance issue, while our second model only uses comprehensive structural features of the sparse matrix. Our method delivers high performance stability for SpMV across different platforms and sparse matrices, due to its application and architecture awareness. Our experimental results demonstrate that a) our approach is able to distinguish and appropriately optimize special matrices in multicore platforms that fall out of the standard class of memory bandwidth bound matrices, and b) lead to a significant performance gain of 29% in a manycore platform compared to an architecture-centric optimization, as a result of the successful selection of the appropriate optimization for the great majority of the matrices. With a runtime overhead equivalent to a couple dozen SpMV iterations, our approach is practical for use in iterative numerical solvers of real-life applications.