AMULET: Adaptive Matrix-Multiplication-Like Tasks

• URL: http://arxiv.org/abs/2305.08872v1
• Date: Fri, 12 May 2023 17:04:24 GMT
• Title: AMULET: Adaptive Matrix-Multiplication-Like Tasks
• Authors: Junyoung Kim, Kenneth Ross, Eric Sedlar, Lukas Stadler
• Abstract summary: We extend an open-source compiler to recognize and optimize matrix multiplication-like tasks. Our framework, called Amulet, uses both database-style and compiler optimization techniques. Amulet typically performs within 15% of hand-tuned matrix multiplication libraries, while handling a much broader class of computations.
• Score: 6.094431019524036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many useful tasks in data science and machine learning applications can be written as simple variations of matrix multiplication. However, users have difficulty performing such tasks as existing matrix/vector libraries support only a limited class of computations hand-tuned for each unique hardware platform. Users can alternatively write the task as a simple nested loop but current compilers are not sophisticated enough to generate fast code for the task written in this way. To address these issues, we extend an open-source compiler to recognize and optimize these matrix multiplication-like tasks. Our framework, called Amulet, uses both database-style and compiler optimization techniques to generate fast code tailored to its execution environment. We show through experiments that Amulet achieves speedups on a variety of matrix multiplication-like tasks compared to existing compilers. For large matrices Amulet typically performs within 15% of hand-tuned matrix multiplication libraries, while handling a much broader class of computations.

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