Javascript must be enabled to continue!
Memory Utilization and Machine Learning Techniques for Compiler Optimization
View through CrossRef
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.
Title: Memory Utilization and Machine Learning Techniques for Compiler Optimization
Description:
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.
Related Results
Selection of Injectable Drug Product Composition using Machine Learning Models (Preprint)
Selection of Injectable Drug Product Composition using Machine Learning Models (Preprint)
BACKGROUND
As of July 2020, a Web of Science search of “machine learning (ML)” nested within the search of “pharmacokinetics or pharmacodynamics” yielded over 100...
CREATING LEARNING MEDIA IN TEACHING ENGLISH AT SMP MUHAMMADIYAH 2 PAGELARAN ACADEMIC YEAR 2020/2021
CREATING LEARNING MEDIA IN TEACHING ENGLISH AT SMP MUHAMMADIYAH 2 PAGELARAN ACADEMIC YEAR 2020/2021
The pandemic Covid-19 currently demands teachers to be able to use technology in teaching and learning process. But in reality there are still many teachers who have not been able ...
The verified CakeML compiler backend
The verified CakeML compiler backend
AbstractThe CakeML compiler is, to the best of our knowledge, the most realistic verified compiler for a functional programming language to date. The architecture of the compiler, ...
Hardware support for dynamic activation of compiler-directed computation reuse
Hardware support for dynamic activation of compiler-directed computation reuse
Compiler-directed Computation Reuse (CCR) enhances program execution speed and efficiency by eliminating dynamic computation redundancy. In this approach, the compiler designates l...
A study of compiler techniques for multiple targets in compiler infrastructures
A study of compiler techniques for multiple targets in compiler infrastructures
Compilers are critical for embedded systems and high performance computing. A compiler infrastructure provides an infrastructure for rapid development of high quality compilers. Ba...
Collective optimization
Collective optimization
Iterative optimization is a popular and efficient research approach to optimize programs using feedback-directed compilation. However, one of the key limitations that prevented wid...
Technology Focus: Data Analytics (October 2021)
Technology Focus: Data Analytics (October 2021)
With a moderate- to low-oil-price environment being the new normal, improving process efficiency, thereby leading to hydrocarbon recovery at reduced costs, is becoming the need of ...
Mapping Ada onto embedded systems: memory constraints
Mapping Ada onto embedded systems: memory constraints
Running Ada programs on a self-targeting system with "virtually" unlimited memory (such as a mainframe), is quite different from running Ada on an embedded target. On self-targetin...

