Paper Info
Title
Detecting coarse-grain parallelism using an interprocedural parallelizing compiler
Abstract
This paper presents an extensive empirical evaluation of an interprocedural parallelizing compiler, developed as part of the Stanford SUIF compiler system. The system incorporates a comprehensive and integrated collection of analyses, including privatization and reduction recognition for both array and scalar variables, and symbolic analysis of array subscripts. The interprocedural analysis framework is designed to provide analysis results nearly as precise as full inlining but without its associated costs. Experimentation with this system shows that it is capable of detecting coarser granularity of parallelism than previously possible. Specifically, it can parallelize loops that span numerous procedures and hundreds of lines of codes, frequently requiring modifications to array data structures such as privatization and reduction transformations. Measurements from several standard benchmark suites demonstrate that an integrated combination of interprocedural analyses can substantially advance the capability of automatic parallelization technology.
Year
DOI
Venue
1995
10.1145/224170.224337
SC
Keywords
Field
DocType
automatic parallelization,compiler optimization,data flow analysis,data analysis,symbolic analysis,compiler optimizations,propulsion,data structures,data structure,lines of code,algorithm design and analysis
Data structure,Interprocedural optimization,Programming language,Computer science,Parallel computing,Scalar (physics),Compiler,Optimizing compiler,Symbolic data analysis,Granularity,Automatic parallelization
Conference
ISSN
ISBN
Citations 
1063-9535
0-89791-816-9
87
PageRank 
References 
Authors
11.43
15
5
Name
Order
Citations
PageRank
Mary W. Hall12328263.34
Saman P. Amarasinghe24734395.55
Brian R. Murphy360884.68
Shih-wei Liao470392.73
Monica S. Lam55585705.61