Computational science

Algorithm

Overhead (engineering)

Theoretical computer science

Subroutine

Computation

Parallel computing

Computer science

Software

Kernel (algebra)

Computer architecture

Mathematics

Linear algebra

Artificial intelligence

Geometry

Combinatorics

Programming language

Artificial neural network

Convolution (computer science)

General Matrix Multiplication (GEMM) is a key subroutine in highperformance computing. While the mainstream linear algebra libraries can deliver high performance on large and regular-shaped GEMM, they are inadequate for optimizing small and irregular-shaped GEMMs, which are commonly seen in new HPC applications. Some of the recent works in this direction have made promising progress on x86 architectures and GPUs but still leave much room for improvement on emerging HPC hardware built upon the ARMv8 architecture. We present LibShalom, an open-source library for optimizing small and irregular-shaped GEMMs, explicitly targeting the ARMv8 architecture. LibShalom builds upon the classical Goto algorithm but tailors it to minimize the expensive memory accessing overhead for data packing and processing small matrices. It uses analytic methods to determine GEMM kernel optimization parameters, enhancing the computation and parallelization efficiency of the GEMM kernels. We evaluate LibShalom by applying it to three ARMv8 multi-core architectures and comparing it against five mainstream linear algebra libraries. Experimental results show that LibShalom can consistently outperform existing solutions across GEMM workloads and hardware architectures.

LIBSHALOM