Abstract
This paper presents adaptive execution techniques that determine whether automatically parallelized loops are executed parallelly or sequentially in order to maximize performance and scalability. The adaptation and performance estimation algorithms are implemented in a compiler preprocessor. The preprocessor inserts code that automatically determines at compile-time or at run-time the way the parallelized loops are executed. Using a set of standard numerical applications written in Fortran77 and running them with our techniques on a distributed shared memory multiprocessor machine (SGI Origin2000), we obtain the performance of our techniques, on average, 26%, 20%, 16%, and 10% faster than the original parallel program on 32, 16, 8, and 4 processors, respectively. One of the applications runs even more than twice faster than its original parallel version on 32 processors.
This work was supported in part by National Science Foundation under grant EIA-0130724 and by National Computational Science Alliance under grant ocn, and utilized the Silicon Graphics Origin2000. This work was also supported in part by the Korean Ministry of Education under the BK21 program and by the Korean Ministry of Science and Technology under the National Research Laboratory program.
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Lee, J., Moonesinghe, H.D.K. (2005). Adaptively Increasing Performance and Scalability of Automatically Parallelized Programs. In: Pugh, B., Tseng, CW. (eds) Languages and Compilers for Parallel Computing. LCPC 2002. Lecture Notes in Computer Science, vol 2481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596110_14
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DOI: https://doi.org/10.1007/11596110_14
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