Performance search engine driven by prior knowledge of optimization
For scientific array-based programs, optimization for a particular target platform is a hard problem. There are many optimization techniques such as (semantics-preserving) source code transformations, compiler directives, environment variables, and compiler flags that influence performance. Moreover, the performance impact of (combinations of) these factors is unpredictable. This pa- per focuses on providing a platform for automatically searching through search space consisting of such optimization techniques. We provide (i) a search-space description language, which enables the user to describe optimization options to be used; (ii) search engine that enables testing the performance impact of optimization options by executing optimized programs and checking their results; and (iii) an interface for implementing various search algorithms. We evaluate our platform by using two simple search algorithms - a random search and a casetree search that heuristically learns from the already examined parts of the search space. We show that such algorithms are easily implementable in our plat- form, and we empirically find that the framework can be used to find useful optimized algorithms.
document
http://n2t.net/ark:/85065/d7g44s2s
eng
geoscientificInformation
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publication
2016-01-01T00:00:00Z
publication
2015-07-01T00:00:00Z
Copyright 2015 Author(s). This is the author’s version of the work. It is posted here for your personal use. Not for redistribution. The definitive version was published in the Proceedings of the 2nd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming, http://dx.doi.org/10.1145/2774959.2774963
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