PolyGym: Polyhedral Optimizations as an Environment for Reinforcement Learning

Brauckmann, Alexander; Goens, Andrés; Castrillón, Jerónimo

doi:10.1109/pact52795.2021.00009

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…The most used machine learning approach recently is the reinforcement learning model like DQN(Deep Q learning) [27]in phase ordering problems. Autophase [28] and some LLVM phase ordering research [29] [30] had shown that well-decision phase ordering could enhance the performance and reduce the code size.…”

Section: Related Workmentioning

confidence: 99%

Accelerating Halide framework by automatic Affine scheduling using the stochastic optimization algorithm on MLIR

Wang,

Shen,

Liao

2023

Preprint

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The increasing usage of computer vision algorithms in camera-centric devices has led to a growing need for optimizing these algorithms to improve their performance on resource-constrained platforms. Halide is a language specific to image processing algorithms that separates the algorithm's scheduling from its implementation, resulting in high performance. This thesis proposes an approach to improve the performance of Halide computer vision algorithms using stochastic algorithms such as simulated annealing to optimize scheduling, which enables exploring the global optimum of affine scheduling in constrained time. To convert the Halide program to MLIR, we use novel compile flows, namely the Halide to MLIR (HTM) converter. The efficacy of the approach will be evaluated on different platforms, such as x86, ARM, and RISC-V. The study demonstrates the potential of MLIR's transformation and optimization capabilities on Affine dialects and highlights the need for tuning infrastructure to fully leverage MLIR's optimization capabilities.

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Section: Related Workmentioning

confidence: 99%

Accelerating Halide framework by automatic Affine scheduling using the stochastic optimization algorithm on MLIR

Wang,

Shen,

Liao

2023

Preprint

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CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

Cummins¹,

Wasti²,

Guo³

et al. 2022

2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)

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Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of software engineering and coding tasks. However, their application in the domain of code and compiler optimization remains underexplored. Training LLMs is resource-intensive, requiring substantial GPU hours and extensive data collection, which can be prohibitive. To address this gap, we introduce Meta Large Language Model Compiler (LLM Compiler), a suite of robust, openly available, pre-trained models specifically designed for code optimization tasks. Built on the foundation of Code Llama, LLM Compiler enhances the understanding of compiler intermediate representations (IRs), assembly language, and optimization techniques. The model has been trained on a vast corpus of 546 billion tokens of LLVM-IR and assembly code and has undergone instruction fine-tuning to interpret compiler behavior. LLM Compiler is released under a bespoke commercial license to allow wide reuse and is available in two sizes: 7 billion and 13 billion parameters. We also present fine-tuned versions of the model, demonstrating its enhanced capabilities in optimizing code size and disassembling from x86_64 and ARM assembly back into LLVM-IR. These achieve 77% of the optimising potential of an autotuning search, and 45% disassembly round trip (14% exact match). This release aims to provide a scalable, cost-effective foundation for further research and development in compiler optimization by both academic researchers and industry practitioners.

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An Experimental Analysis of RL based Compiler Optimization Techniques using Compiler GYM

Nikith,

Reeha,

Reddy

et al. 2024

2024 IEEE 9th International Conference for Convergence in Technology (I2CT)

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PolyGym: Polyhedral Optimizations as an Environment for Reinforcement Learning

Cited by 9 publications

References 24 publications

Accelerating Halide framework by automatic Affine scheduling using the stochastic optimization algorithm on MLIR

Accelerating Halide framework by automatic Affine scheduling using the stochastic optimization algorithm on MLIR

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

An Experimental Analysis of RL based Compiler Optimization Techniques using Compiler GYM

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