Machine-Learning-Based Self-Optimizing Compiler Heuristics✱
Abstract
References
Index Terms
- Machine-Learning-Based Self-Optimizing Compiler Heuristics✱
Recommendations
Improving Vectorization Heuristics in a Dynamic Compiler with Machine Learning Models
VMIL 2022: Proceedings of the 14th ACM SIGPLAN International Workshop on Virtual Machines and Intermediate LanguagesOptimizing compilers rely on many hand-crafted heuristics to guide the optimization process. However, the interactions between different optimizations makes their design a difficult task. We propose using machine learning models to either replace such ...
An Exploratory Study on Machine-Learning-Based Hyper-heuristics for the Knapsack Problem
Pattern RecognitionAbstractHyper-heuristics have risen as a recurrent method to solve combinatorial optimization problems since they use a set of heuristics selectively according to the problem state. Although many ideas have been developed to produce hyper-heuristics, a ...
Continuous learning of compiler heuristics
Special Issue on High-Performance Embedded Architectures and CompilersOptimizing programs to exploit the underlying hardware architecture is an important task. Much research has been done on enabling compilers to find the best set of code optimizations that can build the fastest and less resource-hungry executable for a ...
Comments
Information & Contributors
Information
Published In
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Oracle Labs
Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 737Total Downloads
- Downloads (Last 12 months)486
- Downloads (Last 6 weeks)45
Other Metrics
Citations
Cited By
View allView Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatGet Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in