Abstract
The PMLS parallelizing compiler for Standard ML is based upon the automatic instantiation of algorithmic skeletons at sites of higher order function use. PMLS seeks to optimise run-time parallel be- haviour by combining skeleton cost models with Structural Operational Semantics rule counts for HOF argument functions. In this paper, the formulation of a general rule count cost model as a set of over-determined linear equations is discussed, and their solution by singular value decom- position, and by a genetic algorithm, are presented.
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Scaife, N., Michaelson, G., Horiguchi, S. (2005). Empirical Parallel Performance Prediction from Semantics-Based Profiling. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, vol 3515. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11428848_100
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DOI: https://doi.org/10.1007/11428848_100
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