DocumentCode
2523209
Title
Identifying parallelism in programs with cyclic graphs
Author
Hwang, Yuan-Shin ; Saltz, Joel
Author_Institution
Dept. of Comput. Sci., Nat. Taiwan Ocean Univ., Keelung, Taiwan
fYear
2000
fDate
2000
Firstpage
201
Lastpage
208
Abstract
Dependence analysis algorithms have been proposed to identify parallelism in programs with tree-like data structures. However, they can not analyze the dependence of statements if recursive data structures of programs are cyclic. This paper presents a technique to identify parallelism in programs with cyclic graphs. The technique consists of three steps: (1) Traversal patterns that loops or recursive procedures traverse graphs are identified, and the statements that construct the links of traversal patterns are located by definition-use chains of recursive data structures; (2) Shape analysis is performed to estimate possible shapes of traversal patterns; (3) Dependence analysis is performed to identify parallelism using the result of shape analysis. This approach can identify parallelism in programs with cyclic data structures due to the facts that many programs follow acyclic structures (i.e. traversal patterns) to access all nodes on the cyclic data structures. Once the traversal patterns are isolated from the overall data structures, dependence analysis can be applied to identify parallelism
Keywords
parallel programming; tree data structures; cyclic data structures; cyclic graphs; definition-use chains; dependence analysis; dependence analysis algorithms; parallelism; recursive data structures; shape analysis; traversal patterns; tree-like data structures; Algorithm design and analysis; Data analysis; Data structures; Pattern analysis; Performance analysis; Recursive estimation; Shape; State estimation; Tree data structures; Tree graphs;
fLanguage
English
Publisher
ieee
Conference_Titel
Parallel Processing, 2000. Proceedings. 2000 International Conference on
Conference_Location
Toronto, Ont.
ISSN
0190-3918
Print_ISBN
0-7695-0768-9
Type
conf
DOI
10.1109/ICPP.2000.876120
Filename
876120
Link To Document