Abstract
This paper describes a platform independent optimisation approach based on feedback-directed program restructuring. We have developed two strategies that search the optimisation space by means of profiling to find the best possible program variant. These strategies have no a priori knowledge of the target machine and can be run on any platform. In this paper our approach is evaluated on three full SPEC benchmarks, rather than the kernels evaluated in earlier studies where the optimisation space is relatively small. This approach was evaluated on six different platforms, where it is shown that we obtain on average a 20.5% reduction in execution time compared to the native compiler with full optimisation. By using training data instead of reference data for the search procedure, we can reduce compilation time and still give on average a 16.5% reduction in time when running on reference data. We show that our approach is able to give similar significant reductions in execution time over a state of the art high level restructurer based on static analysis and a platform specific profile feedback directed compiler that employs the same transformations as our iterative system.
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© 2005 Springer-Verlag Berlin Heidelberg
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Fursin, G.G., O’Boyle, M.F.P., Knijnenburg, P.M.W. (2005). Evaluating Iterative Compilation. 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_24
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DOI: https://doi.org/10.1007/11596110_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30781-5
Online ISBN: 978-3-540-31612-1
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