Abstract
A dynamic evaluation of the effects of data dependence analysis in the Perfect Benchmarks is demonstrated. We show that it is possible to measure the optimal parallelism, as defined by our model, and to compare the obtained parallelism for various data dependence tests with the optimal parallelism. We find that a variation of Banerjee's inequalities is sufficient in all cases to obtain more than half of the available parallelism, and that the Omega test does not contribute significantly to the measured parallelism.
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© 1993 Springer-Verlag Berlin Heidelberg
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Peterson, P., Padua, D. (1993). Dynamic dependence analysis: A novel method for data dependence evaluation. In: Banerjee, U., Gelernter, D., Nicolau, A., Padua, D. (eds) Languages and Compilers for Parallel Computing. LCPC 1992. Lecture Notes in Computer Science, vol 757. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57502-2_40
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DOI: https://doi.org/10.1007/3-540-57502-2_40
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