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We explore the utilization of the Apache TVM open source framework toautomatically generate a family of algorithms that follow the approach taken bypopular linear algebra libraries, such as GotoBLAS2, BLIS and OpenBLAS, inorder to obtain high-performance blocked formulations of the general matrixmultiplication (GEMM). % In addition, we fully automatize the generationprocess, by also leveraging the Apache TVM framework to derive a completevariety of the processor-specific micro-kernels for GEMM. This is in contrastwith the convention in high performance libraries, which hand-encode a singlemicro-kernel per architecture using Assembly code. % In global, the combinationof our TVM-generated blocked algorithms and micro-kernels for GEMM 1)~improvesportability, maintainability and, globally, streamlines the software lifecycle; 2)~provides high flexibility to easily tailor and optimize the solutionto 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.

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