Characterizing Memory-Latency Sensitivity of Sparse Matrix Kernels

Abstract

Intel announced to launch a Xeon with high-latency main memory based on 3D Xpoint in 2018. This paper presents the performance evaluation of sparse matrix kernels on the future supercomputers with high-latency main memory such as 3D Xpoint. The authors propose a high throughput evaluation methodology for exhaustive experiments, which use the University of Florida sparse matrix collection and/or LIS (a Library of Iterative Solvers for linear systems) etc. Proposed methodology is very simple to use, highly flexible for environment and high-throughput. Latency sensitivity of SpMV is measured based on the proposed methodology with 208 sparse matrices and ten storage formats only in two days, which would take for about ten years by conventional simulators. We got several interesting knowledge about latency-sensitive kernels, sparse matrices, storage formats, and preconditioners, etc. We observed notable latency sensitivity in some applications, which are Graph500, HPCG and a part of preconditioners of iterative solvers. We found latency sensitivities of SpMV are high for larger matrices than the capacity of last level cache. This suggests main memory using 3D Xpoint must be combined with large DRAM cache.