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Author Bala, Kavita ♦ Kulkarni, Milind ♦ Chew, L. Paul ♦ Ramanarayanan, Ganesh ♦ Pingali, Keshav ♦ Walter, Bruce
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Abstract The problem of writing software for multicore processors is greatly simplified if we could automatically parallelize sequential programs. Although auto-parallelization has been studied for many decades, it has succeeded only in a few application areas such as dense matrix computations. In particular, auto-parallelization of irregular programs, which are organized around large, pointer-based data structures like graphs, has seemed intractable. The Galois project is taking a fresh look at autoparallelization. Rather than attempt to parallelize all programs no matter how obscurely they are written, we are designing programming abstractions that permit programmers to highlight opportunities for exploiting parallelism in sequential programs, and building a runtime system that uses these hints to execute the program in parallel. In this paper, we describe the design and implementation of a system based on these ideas. Experimental results for two real-world irregular applications, a Delaunay mesh refinement application and a graphics application that performs agglomerative clustering, demonstrate that this approach is promising.
Description Affiliation: Cornell University, Ithaca, NY (Walter, Bruce; Ramanarayanan, Ganesh; Bala, Kavita; Chew, L. Paul) || University of Texas, Austin (Kulkarni, Milind; Pingali, Keshav)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-08-01
Publisher Place New York
Journal Communications of the ACM (CACM)
Volume Number 52
Issue Number 9
Page Count 9
Starting Page 89
Ending Page 97


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Source: ACM Digital Library