Characterizing Small-Scale Matrix Multiplications on ARMv8-based Many-Core Architectures
Weiling Yang, Jianbin Fang, Dezun Dong · 2021
General Matrix Multiplication (GEMM) is a key subroutine in high-performance computing. There is a large body of work on evaluating and optimizing large-scale matrix multiplication, but how well the small-scale matrix multiplication (SMM) performs is largely unknown, especially for the ARMv8-based many-core architectures. In this work, we evaluate and characterize the performance of SMM subroutines on Phytium 2000 +, an ARMv8-based 64-core architecture. The evaluation work is extensively performed with the mainstream open-source libraries including OpenBLAS, BLIS, BALSFEO, and Eigen. Given various experimental settings, we observe how well the small-scale GEMM routines perform on Phytium 2000 +, and then discuss the impacting factors behind the performance behaviours of SMM. Built on such a basis, we shed light on the performance bottlenecks and practical optimizations on SMM from various angles: (1) mitigating the data packing overhead, (2) processing the edge cases properly, (3) selecting a suitable micro-kernel, and (4) adopting a right parallelization method. The result of our work facilitates users to develop efficient SMM optimizations on ARMv8-based many-core architectures, and embed them into real-world applications.