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.
Sat 13 JunDisplayed time zone: Tijuana, Baja California change
14:00 - 15:30 | |||
14:00 30mTalk | Performance Search Engine Driven by Prior Knowledge of Optimization ARRAY | ||
14:30 30mTalk | High-Level Accelerated Array Programming in the Web Browser ARRAY | ||
15:00 30mTalk | Accelerating Information Experts through Compiler Design ARRAY |