NDLI: Compiler-assisted dynamic scheduling for effective parallelization of loop nests on multicore processors

Content Provider	ACM Digital Library
Author	Ramanujam, J. Rountev, Atanas Vydyanathan, Nagavijayalakshmi Baskaran, Muthu Manikandan Sadayappan, P. Bondhugula, Uday Kumar Reddy
Abstract	Recent advances in polyhedral compilation technology have made it feasible to automatically transform affine sequential loop nests for tiled parallel execution on multi-core processors. However, for multi-statement input programs with statements of different dimensionalities, such as Cholesky or LU decomposition, the parallel tiled code generated by existing automatic parallelization approaches may suffer from significant load imbalance, resulting in poor scalability on multi-core systems. In this paper, we develop a completely automatic parallelization approach for transforming input affine sequential codes into efficient parallel codes that can be executed on a multi-core system in a load-balanced manner. In our approach, we employ a compile-time technique that enables dynamic extraction of inter-tile dependences at run-time, and dynamic scheduling of the parallel tiles on the processor cores for improved scalable execution. Our approach obviates the need for programmer intervention and re-writing of existing algorithms for efficient parallel execution on multi-cores. We demonstrate the usefulness of our approach through comparisons using linear algebra computations: LU and Cholesky decomposition.
Starting Page	219
Ending Page	228
Page Count	10
File Format	PDF
ISSN	03621340 15581160
DOI	10.1145/1594835.1504209
Journal	ACM SIGPLAN Notices (SIGP)
Volume Number	44
Issue Number	4
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1983-05-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Compile-time optimization Dynamic scheduling Run-time optimization
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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