Machine-Learning-Based Self-Optimizing Compiler Heuristics✱-Reference-Cited by-同舟云学术

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Machine-Learning-Based Self-Optimizing Compiler Heuristics✱

Published:2022-09-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 19th International Conference on Managed Programming Languages and Runtimes
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Author:

Mosaner Raphael¹^ORCID,Leopoldseder David²^ORCID,Kisling Wolfgang¹^ORCID,Stadler Lukas²,Mössenböck Hanspeter¹

Affiliation:

1. Johannes Kepler University, Austria

2. Oracle Labs, Austria

Funder

Oracle Labs

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3546918.3546921

Reference48 articles.

1. Finding effective compilation sequences

2. Amir H. Ashouri , William Killian , John Cavazos , Gianluca Palermo , and Cristina Silvano . 2018. A Survey on Compiler Autotuning Using Machine Learning. ACM Comput. Surv. 51, 5 , Article 96 (Sept. 2018 ), 42 pages. https://doi.org/10.1145/3197978 10.1145/3197978 Amir H. Ashouri, William Killian, John Cavazos, Gianluca Palermo, and Cristina Silvano. 2018. A Survey on Compiler Autotuning Using Machine Learning. ACM Comput. Surv. 51, 5, Article 96 (Sept. 2018), 42 pages. https://doi.org/10.1145/3197978

3. Fast;Auslander Joel;Effective Dynamic Compilation. SIGPLAN Not.,1996

4. Compiler transformations for high-performance computing

5. The DaCapo benchmarks

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Diagnosing Compiler Performance by Comparing Optimization Decisions;Proceedings of the 20th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes;2023-10-19

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