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 transfor-mations, compiler directives, environment variables, and compiler flags that influence performance. Moreover, the performance im-pact 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 optimiza-tion options by executing optimized programs and checking their results; and (iii) an interface for implementing various search al-gorithms. We evaluate our platform by using two simple search algorithms- a random search and a casetree search that heuristi-cally 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