Some efficient solutions to the affine scheduling problem: I. One-dimensional time
International Journal of Parallel Programming
An affine partitioning algorithm to maximize parallelism and minimize communication
ICS '99 Proceedings of the 13th international conference on Supercomputing
Optimizing compilers for modern architectures: a dependence-based approach
Optimizing compilers for modern architectures: a dependence-based approach
Parallel Processing: From Applications to Systems
Parallel Processing: From Applications to Systems
On Privatization of Variables for Data-Parallel Execution
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
Proceedings of the 6th International Workshop on Languages and Compilers for Parallel Computing
An Exact Method for Analysis of Value-based Array Data Dependences
Proceedings of the 6th International Workshop on Languages and Compilers for Parallel Computing
A practical automatic polyhedral parallelizer and locality optimizer
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
CC'08/ETAPS'08 Proceedings of the Joint European Conferences on Theory and Practice of Software 17th international conference on Compiler construction
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Privatization of data is an important technique that has been used by compilers to parallelize loops by eliminating storage-related dependences. The code can be executed on multi-processors machines in reduced period of time. In this paper, we present an approach to automatic privatization of variables involved in data dependences that permits for extracting loop parallelism. The input of the algorithm is a set of relation dependences, the output is a parallel loop when appropriate. The scope of the applicability of the approach is illustrated by means of the NAS Parallel Benchmark suite. Received results are compared with those produced by the tool Pluto. Future work is outlined.