Active-Learning-Based Surrogate Models for Empirical Performance Tuning

TitleActive-Learning-Based Surrogate Models for Empirical Performance Tuning
Publication TypeConference Paper
Year of Publication2013
AuthorsBalaprakash, P, Gramacy, RB, Wild, SM
Conference NameIEEE Cluster 2013
PublisherIEEE
Conference LocationIndianapolis, IN
Other NumbersANL/MCS-P4073-0513
Abstract

Performance models have profound impact on hardware-software codesign, architectural explorations, and performance tuning of scientific applications. Developing algebraic performance models is becoming an increasingly challenging task. In such situations, a statistical surrogate-based performance model, fitted to a small number of input-output points obtained from empirical evaluation on the target machine, provides a range of benefits. Accurate surrogates can emulate the output of the expensive empirical evaluation at new inputs and therefore can be used to test and/or aid search, compiler, and autotuning algorithms. We present an iterative parallel algorithm that builds surrogate performance models for scientific kernels and work-loads on single-core and multicore and multinode architectures. We tailor to our unique parallel environment an active learning heuristic popular in the literature on the sequential design of computer experiments in order to identify the code variants whose evaluations have the best potential to improve the surrogate. We use the proposed approach in a number of case studies to illustrate its effectiveness.

 

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