Automatic Generators for a Family of Matrix Multiplication Routines with Apache TVM

• URL: http://arxiv.org/abs/2310.20347v1
• Date: Tue, 31 Oct 2023 10:36:26 GMT
• Title: Automatic Generators for a Family of Matrix Multiplication Routines with Apache TVM
• Authors: Guillermo Alaejos, Adri\'an Castell\'o, Pedro Alonso-Jord\'a, Francisco D. Igual, H\'ector Mart\'inez, Enrique S. Quintana-Ort\'i
• Abstract summary: We generate a family of algorithms that follow the approach taken by popular linear algebra libraries, such as GotoBLAS2, BLIS and OpenBLAS. We also leverage the Apache TVM framework to derive a complete variety of the processor-specific micro- Kernels for GEMM.
• Score: 0.20971479389679337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We explore the utilization of the Apache TVM open source framework to automatically generate a family of algorithms that follow the approach taken by popular linear algebra libraries, such as GotoBLAS2, BLIS and OpenBLAS, in order to obtain high-performance blocked formulations of the general matrix multiplication (GEMM). % In addition, we fully automatize the generation process, by also leveraging the Apache TVM framework to derive a complete variety of the processor-specific micro-kernels for GEMM. This is in contrast with the convention in high performance libraries, which hand-encode a single micro-kernel per architecture using Assembly code. % In global, the combination of our TVM-generated blocked algorithms and micro-kernels for GEMM 1)~improves portability, maintainability and, globally, streamlines the software life cycle; 2)~provides high flexibility to easily tailor and optimize the solution to different data types, processor architectures, and matrix operand shapes, yielding performance on a par (or even superior for specific matrix shapes) with that of hand-tuned libraries; and 3)~features a small memory footprint.

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