Title

Preliminary Results For Neuroevolutionary Optimization Phase Order Generation For Static Compilation

Keywords

DXNN; Evolutionary computation; LLVM; Neural network; Neuroevolution; Optimization; Optimization phase ordering

Abstract

There is a complex web of interactions between optimization phases in static program compilation. Because there are many different types of optimizations, and each changes the form of the program and can impact the result of subsequent optimizations, the selection of optimizations to apply is challenging and is known as the "optimization phase ordering problem." There is a need to effectively optimize the order of the optimizations and specific optimizations used based on the statistics and other features of the program to gain the most benefit. In this work we propose the use of evolved neural networks to intelligently choose which optimizations are applied and in what order, to a method or a program as a whole, based on its features. In this paper we study the use of the memetic algorithm-based neuroevolutionary system called DXNN, and a genetic algorithm based neuroevolutionary system called NEAT, to evolve such neural networks. Copyright 2014 ACM.

Publication Date

1-1-2014

Publication Title

ACM International Conference Proceeding Series

Number of Pages

33-40

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2568326.2568328

Socpus ID

84898783693 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84898783693

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