Interprocedural dependence analysis and parallelization
SIGPLAN '86 Proceedings of the 1986 SIGPLAN symposium on Compiler construction
On the accuracy of the Banerjee test
Journal of Parallel and Distributed Computing - Special issue on shared-memory multiprocessors
PLDI '91 Proceedings of the ACM SIGPLAN 1991 conference on Programming language design and implementation
A practical algorithm for exact array dependence analysis
Communications of the ACM
Eliminating false data dependences using the Omega test
PLDI '92 Proceedings of the ACM SIGPLAN 1992 conference on Programming language design and implementation
The Banerjee-Wolfe and GCD tests on exact data dependence information
Journal of Parallel and Distributed Computing
Dependence Analysis for Supercomputing
Dependence Analysis for Supercomputing
Parallel Programming with Polaris
Computer
The I Test: An Improved Dependence Test for Automatic Parallelization and Vectorization
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
A unified framework for nonlinear dependence testing and symbolic analysis
Proceedings of the 18th annual international conference on Supercomputing
Efficient Techniques for Advanced Data Dependence Analysis
Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques
Data dependence analysis techniques for increased accuracy and extracted parallelism
International Journal of Parallel Programming - Special issue II: The 17th annual international conference on supercomputing (ICS'03)
Three-dimensional delaunay refinement for multi-core processors
Proceedings of the 22nd annual international conference on Supercomputing
A data dependence test based on the projection of paths over shape graphs
Journal of Parallel and Distributed Computing
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Optimizing compilers rely upon program analysis techniques to detect data dependences between program statements. Data dependence information captures the essential ordering constraints of the statements in a program that need to be preserved in order to produce valid optimized and parallel code. Data dependence testing is very important for automatic parallelization, vectorization and any other code transformation. In this paper we examine the impact of data dependence analysis in practice. A number of data dependence tests have been proposed in the literature. In each test there are different tradeoffs between accuracy and efficiency. We present an experimental evaluation of several data dependence tests, including the Banerjee test, the I-Test and the Omega test. We compare these tests in terms of data dependence accuracy, compilation efficiency, effectiveness in parallelization and program execution performance. We analyze the reasons why a data dependence test can be inexact and we explain how the examined tests handle such cases. We run various experiments using the Perfect Club Benchmarks and the scientific library Lapack. We present the measured accuracy of each test and the reasons for any approximation. We compare these tests in terms of efficiency and we analyze the tradeoffs between accuracy and efficiency. We also determine the impact of each data dependence test on the total compilation time. Finally, we measure the number of loops parallelized by each test and we compare the execution performance of each benchmark on a multiprocessor. Our results indicate that the Omega test is more accurate, but also very inefficient in the cases where the other two tests are inaccurate. In general the cost of the Omega test is high and a significant percentage of the total compilation time. Furthermore, the difference in accuracy of the Omega test over the Banerjee test and the I-Test does not improve parallelization and program execution performance.