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Input Sensitivity of GPU Program Optimizations

Yixun Liu, Eddy Z. Zhang, Poornima Bhamidipati, Xipeng Shen
Computer Science Department, College of William and Mary
Cetus Users and Compiler Infastructure Workshop, PACT 2011

@article{liu2011input,

   title={Input Sensitivity of GPU Program Optimizations},

   author={Liu, Y. and Zhang, E.Z. and Bhamidipati, P. and Shen, X.},

   booktitle={Cetus Users and Compiler Infastructure Workshop, PACT 2011},

   year={2011}

}

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Graphic Processing Units (GPU) have become increasingly adopted for the enhancement of computing throughput. However, the development of a high-quality GPU application is challenging, due to the large optimization space and complex unpredictable effects of optimizations on GPU program performance. Many recent efforts have been employing empirical search-based auto-tuners to tackle the problem, but few of them have concentrated on the influence of program inputs on the optimizations. In this paper, based on a set of CUDA and OpenCL kernels, we report some evidences on the importance for autotuners to adapt to program input changes, and present a framework, GADAPT+, to address the influence by constructing cross-input predictive models for automatically predicting the (near-)optimal configurations for an arbitrary input to a GPU program. G-ADAPT+ is based on source-to-source compilers, specifically, Cetus and ROSE. It supports the optimizations of both CUDA and OpenCL programs.
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