Argonne National Laboratory

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Active Learning-based Surrogate Models for Empirical Performance Tuning

Computational Institute Presentation
Prasanna Balaprakash (MCS)
February 13, 2014 12:30PM to 1:30PM
Building 240, Room 5172
This talk will be broadcast to the University of Chicago, Searle 240A, 5735 S. Ellis Ave. You may join the broadcast from your location by using Adobe Connect.

Performance models have profound impact on hardware-software co-design, 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.

Lunch will be provided.