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Learning to superoptimize programs - Workshop Version
[article]
2016
arXiv
pre-print
Superoptimization requires the estimation of the best program for a given computational task. In order to deal with large programs, superoptimization techniques perform a stochastic search. This involves proposing a modification of the current program, which is accepted or rejected based on the improvement achieved. The state of the art method uses uniform proposal distributions, which fails to exploit the problem structure to the fullest. To alleviate this deficiency, we learn a proposal
arXiv:1612.01094v1
fatcat:rjlyc6cflreqhi3p5jlout6ihm