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Author Bacon, David F. ♦ Graham, Susan L. ♦ Sharp, Oliver J.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©1994
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Compilation ♦ Dependence analysis ♦ Locality ♦ Multiprocessors ♦ Optimization ♦ Parallelism ♦ Superscalar processors ♦ Vectorization
Abstract In the last three decades a large number of compiler transformations for optimizing programs have been implemented. Most optimizations for uniprocessors reduce the number of instructions executed by the program using transformations based on the analysis of scalar quantities and data-flow techniques. In contrast, optimizations for high-performance superscalar, vector, and parallel processors maximize parallelism and memory locality with transformations that rely on tracking the properties of arrays using loop dependence analysis.This survey is a comprehensive overview of the important high-level program restructuring techniques for imperative languages, such as C and Fortran. Transformations for both sequential and various types of parallel architectures are covered in depth. We describe the purpose of each transformation, explain how to determine if it is legal, and give an example of its application.Programmers wishing to enhance the performance of their code can use this survey to improve their understanding of the optimizations that compilers can perform, or as a reference for techniques to be applied manually. Students can obtain an overview of optimizing compiler technology. Compiler writers can use this survey as a reference for most of the important optimizations developed to date, and as bibliographic reference for the details of each optimization. Readers are expected to be familiar with modern computer architecture and basic program compilation techniques.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1994-12-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 26
Issue Number 4
Page Count 76
Starting Page 345
Ending Page 420


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Source: ACM Digital Library