Memory Utilization and Machine Learning Techniques for Compiler Optimization

• Published in: ITM Web of Conferences Vol. 37; p. 1021
• Main Authors: Shreyas Madhav, A V, Singaravel, Siddarth, Karmel, A
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2021
• Subjects: Compilers; Computation; Machine learning; Memory management; Optimization; Optimization techniques
• ISSN: 2271-2097; 2431-7578; 2271-2097
• DOI: 10.1051/itmconf/20213701021

Compiler optimization techniques allow developers to achieve peak performance with low-cost hardware and are of prime importance in the field of efficient computing strategies. The realm of compiler suites that possess and apply efficient optimization methods provide a wide array of beneficial attributes that help programs execute efficiently with low execution time and minimal memory utilization. Different compilers provide a certain degree of optimization possibilities and applying the appropriate optimization strategies to complex programs can have a significant impact on the overall performance of the system. This paper discusses methods of compiler optimization and covers significant advances in compiler optimization techniques that have been established over the years. This article aims to provide an overall survey of the cache optimization methods, multi memory allocation features and explore the scope of machine learning in compiler optimization to attain a sustainable computing experience for the developer and user.