PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Better than optimal: fast identification of custom instruction candidates
J. Reddington, G. Gutin, A. Yeo, A. Johnstone and E. Scott
In: 7th IEEE/IFIP International Conference on Embedded and Ubiqutious Computing(2009).


Asymptotically optimal algorithms do not always yield the fastest practical algorithm on realistic cases. We examine Gutin et al.’s recently published optimal algorithm for enumerating the set of convex subgraphs under input/output constraints with application to custom instruction identification. We show that (i) suppressing some of the machinery in their algorithm results in a sub-optimal algorithm which is significantly faster in practice on real-world examples and that (ii) the constants of proportionality in the running time for both optimal and sub-optimal versions can be significantly improved by using additional output set filtering constraints.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6576
Deposited By:Gregory Gutin
Deposited On:08 March 2010