IATF: An Input-Aware Tuning Framework for Compact BLAS Based on ARMv8 CPUs

Abstract

Recently the mainstream basic linear algebra libraries have delivered high performance on large scale General Matrix Multiplication(GEMM) and Triangular System Solve(TRSM). However, these libraries are still insufficient to provide sustained performance for batch operations on large groups of fixed-size small matrices on specific architectures, which are extensively used in various scientific computing applications. In this paper, we propose IATF, an input-aware tuning framework for optimizing large group of fixed-size small GEMM and TRSM to boost near-optimal performance on ARMv8 architecture. The IATF contains two stages: install-time stage and run-time stage. In the install-time stage, based on SIMD-friendly data layout, we propose computing kernel templates for high-performance GEMM and TRSM, analyze optimal kernel sizes to increase computational instruction ratio, and design kernel optimization strategies to improve kernel execution efficiency. Furthermore, an optimized data packing strategy is also presented for computing kernels to minimize the cost of memory accessing overhead. In the run-time stage, we present an input-aware tuning method to generate an efficient execution plan for large group of fixed-size small GEMM and TRSM, according to the input matrix properties. The experimental results show that IATF could achieve significant performance improvements in GEMM and TRSM compared with other mainstream BLAS libraries.