A GSA-based compiler infrastructure to extract parallelism from complex loops
ICS '03 Proceedings of the 17th annual international conference on Supercomputing
An empirical evaluation of chains of recurrences for array dependence testing
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
One-dimensional I test and direction vector I test with array references by induction variable
International Journal of High Performance Computing and Networking
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Data dependence analysis is a fundamental step in an optimizing compiler. The results of the analysis enable the compiler to identify code fragments that can be executed in parallel. A number of data dependence tests have been proposed in the literature. In each test there are different tradeoffs between accuracy and efficiency. In this paper 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 accuracy and efficiency. We run various experiments using the Perfect Club Benchmarks and the scientific libraries Eispack, Linpack and Lapack. Several observations and conclusions are derived from the experimental results, which are displayed and analyzed in this paper.