A Reinforcement Learning Environment for Polyhedral Optimizations

• URL: http://arxiv.org/abs/2104.13732v2
• Date: Thu, 29 Apr 2021 08:04:04 GMT
• Title: A Reinforcement Learning Environment for Polyhedral Optimizations
• Authors: Alexander Brauckmann, Andr\'es Goens, Jeronimo Castrillon
• Abstract summary: We propose a shape-agnostic formulation for the space of legal transformations in the polyhedral model as a Markov Decision Process (MDP) Instead of using transformations, the formulation is based on an abstract space of possible schedules. Our generic MDP formulation enables using reinforcement learning to learn optimization policies over a wide range of loops.
• Score: 68.8204255655161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The polyhedral model allows a structured way of defining semantics-preserving transformations to improve the performance of a large class of loops. Finding profitable points in this space is a hard problem which is usually approached by heuristics that generalize from domain-expert knowledge. Existing problem formulations in state-of-the-art heuristics depend on the shape of particular loops, making it hard to leverage generic and more powerful optimization techniques from the machine learning domain. In this paper, we propose PolyGym, a shape-agnostic formulation for the space of legal transformations in the polyhedral model as a Markov Decision Process (MDP). Instead of using transformations, the formulation is based on an abstract space of possible schedules. In this formulation, states model partial schedules, which are constructed by actions that are reusable across different loops. With a simple heuristic to traverse the space, we demonstrate that our formulation is powerful enough to match and outperform state-of-the-art heuristics. On the Polybench benchmark suite, we found transformations that led to a speedup of 3.39x over LLVM O3, which is 1.83x better than the speedup achieved by ISL. Our generic MDP formulation enables using reinforcement learning to learn optimization policies over a wide range of loops. This also contributes to the emerging field of machine learning in compilers, as it exposes a novel problem formulation that can push the limits of existing methods.

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