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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2312.09982 |
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| _version_ | 1866915101379919872 |
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| author | Ashouri, Amir H. Manzoor, Muhammad Asif Vu, Duc Minh Zhang, Raymond Toft, Colin Wang, Ziwen Zhang, Angel Chan, Bryan Czajkowski, Tomasz S. Gao, Yaoqing |
| author_facet | Ashouri, Amir H. Manzoor, Muhammad Asif Vu, Duc Minh Zhang, Raymond Toft, Colin Wang, Ziwen Zhang, Angel Chan, Bryan Czajkowski, Tomasz S. Gao, Yaoqing |
| contents | The key to performance optimization of a program is to decide correctly when a certain transformation should be applied by a compiler. This is an ideal opportunity to apply machine-learning models to speed up the tuning process; while this realization has been around since the late 90s, only recent advancements in ML enabled a practical application of ML to compilers as an end-to-end framework.
This paper presents ACPO: An AI-Enabled Compiler Framework, a novel framework that provides LLVM with simple and comprehensive tools to benefit from employing ML models for different optimization passes. We first showcase the high-level view, class hierarchy, and functionalities of ACPO and subsequently, demonstrate \taco{a couple of use cases of ACPO by ML-enabling the Loop Unroll and Function Inlining passes used in LLVM's O3. and finally, describe how ACPO can be leveraged to optimize other passes. Experimental results reveal that the ACPO model for Loop Unroll can gain on average 4%, 3%, 5.4%, and 0.2% compared to LLVM's vanilla O3 optimization when deployed on Polybench, Coral-2, CoreMark, and Graph-500, respectively. Furthermore, by including both Function Inlining and Loop Unroll models, ACPO can provide a combined speedup of 4.5% on Polybench and 2.4% on Cbench when compared with LLVM's O3, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_09982 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ACPO: AI-Enabled Compiler Framework Ashouri, Amir H. Manzoor, Muhammad Asif Vu, Duc Minh Zhang, Raymond Toft, Colin Wang, Ziwen Zhang, Angel Chan, Bryan Czajkowski, Tomasz S. Gao, Yaoqing Programming Languages Artificial Intelligence Machine Learning Performance I.2.5; D.3.0; I.2.6 The key to performance optimization of a program is to decide correctly when a certain transformation should be applied by a compiler. This is an ideal opportunity to apply machine-learning models to speed up the tuning process; while this realization has been around since the late 90s, only recent advancements in ML enabled a practical application of ML to compilers as an end-to-end framework. This paper presents ACPO: An AI-Enabled Compiler Framework, a novel framework that provides LLVM with simple and comprehensive tools to benefit from employing ML models for different optimization passes. We first showcase the high-level view, class hierarchy, and functionalities of ACPO and subsequently, demonstrate \taco{a couple of use cases of ACPO by ML-enabling the Loop Unroll and Function Inlining passes used in LLVM's O3. and finally, describe how ACPO can be leveraged to optimize other passes. Experimental results reveal that the ACPO model for Loop Unroll can gain on average 4%, 3%, 5.4%, and 0.2% compared to LLVM's vanilla O3 optimization when deployed on Polybench, Coral-2, CoreMark, and Graph-500, respectively. Furthermore, by including both Function Inlining and Loop Unroll models, ACPO can provide a combined speedup of 4.5% on Polybench and 2.4% on Cbench when compared with LLVM's O3, respectively. |
| title | ACPO: AI-Enabled Compiler Framework |
| topic | Programming Languages Artificial Intelligence Machine Learning Performance I.2.5; D.3.0; I.2.6 |
| url | https://arxiv.org/abs/2312.09982 |