Invited paper: An Artificial Matrix Generator for Multi-platform SpMV Performance Analysis

Sparse Matrix-Vector (SpMV) multiplication is a frequently encountered computational kernel, that is notorious for achieving only a small fraction of system peak performance. The optimization of this kernel and the identification of bottlenecks that limit its performance have been the subject of considerable research efforts. Nevertheless, obtaining an unbiased matrix dataset that can expose these bottlenecks remains a recurring challenge. To this end, we propose a feature-based artificial matrix generation method, that associates the most common SpMV bottlenecks with core matrix features, and develop a generator program that can quickly produce a wide variety of matrices on the fly for performance analysis. To evaluate our generator, we compare the performance of artificial and real matrices, across three different platforms: a CPU, a GPU and an FPGA.