mlsanityData to 14 Oct 2026

Tackling the Matrix Multiplication Micro-kernel Generation with Exo

arXiv 2310.17408 ↗First posted 26 Oct 2023cs.MS cs.CL cs.PFcited by 1

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Abstract (arXiv)

The optimization of the matrix multiplication (or GEMM) has been a need during the last decades. This operation is considered the flagship of current linear algebra libraries such as BLIS, OpenBLAS, or Intel OneAPI because of its widespread use in a large variety of scientific applications. The GEMM is usually implemented following the GotoBLAS philosophy, which tiles the GEMM operands and uses a series of nested loops for performance improvement. These approaches extract the maximum computational power of the architectures through small pieces of hardware-oriented, high-performance code called micro-kernel. However, this approach forces developers to generate, with a non-negligible effort, a dedicated micro-kernel for each new hardware. In this work, we present a step-by-step procedure for generating micro-kernels with the Exo compiler that performs close to (or even better than) manually developed microkernels written with intrinsic functions or assembly language. Our solution also improves the portability of the generated code, since a hardware target is fully specified by a concise library-based description of its instructions.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 36 references that are in the map. First posted on arXiv 26 Oct 2023.

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9 of 36 references.

References · 9

  1. 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 434
  2. 2020Ansor: Generating High-Performance Tensor Programs for Deep Learningcited by 201
  3. 2018Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysiscited by 156
  4. 2018Learning to Optimize Tensor Programscited by 140
  5. 2020MLIR: A Compiler Infrastructure for the End of Moore's Lawcited by 65
  6. 2019Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integrationcited by 63
  7. 2020The Deep Learning Compiler: A Comprehensive Surveycited by 46
  8. 2021CoSA: Scheduling by Constrained Optimization for Spatial Acceleratorscited by 22
  9. 2018A Hardware-Software Blueprint for Flexible Deep Learning Specializationcited by 7

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