Abstract
The sparse matrix-vector product (SpMV) is a fundamental operation in many scientific applications from various fields. The High Performance Computing (HPC) community has therefore continuously invested a lot of effort to provide an efficient SpMV kernel on modern CPU architectures. Although it has been shown that block-based kernels help to achieve high performance, they are difficult to use in practice because of the zero padding they require. In the current paper, we propose new kernels using the AVX-512 instruction set, which makes it possible to use a blocking scheme without any zero padding in the matrix memory storage. We describe mask-based sparse matrix formats and their corresponding SpMV kernels highly optimized in assembly language. Considering that the optimal blocking size depends on the matrix, we also provide a method to predict the best kernel to be used utilizing a simple interpolation of results from previous executions. We compare the performance of our approach to that of the Intel MKL CSR kernel and the CSR5 open-source package on a set of standard benchmark matrices. We show that we can achieve significant improvements in many cases, both for sequential and for parallel executions. Finally, we provide the corresponding code in an open source library, called SPC5.
Highlights
The sparse matrix-vector product (SpMV) is an important operation in many applications, which often needs to be performed multiple times in the course of the algorithm
Since in many scientific applications a large part of the CPU time is spent in the solution of the resulting linear system and the matrix is stored in a sparse manner, improving the efficiency of the SpMV on modern hardware could potentially leverage the performance of a wide range of codes
In ‘SpMV Kernels for β(r,c) storages’, we described generic kernels, which can be used with any block sizes
Summary
The sparse matrix-vector product (SpMV) is an important operation in many applications, which often needs to be performed multiple times in the course of the algorithm. With the effective halt in the growth of processor frequencies, most of the increase of computing power is achieved through increasing the number of cores and the ability of each individual core to perform each operation on a vector of certain length using one instruction only. This capability is named vectorization or single instruction on multiple data (SIMD)
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