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An Infrastructure for Tackling Input-Sensitivity of GPU Program Optimizations

机译：解决GPU程序优化的输入敏感性的基础架构

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摘要

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 auto-tuners to adapt to program input changes, and present a framework, G-ADAPT+, 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.
机译：图形处理单元（GPU）已越来越多地用于提高计算吞吐量。然而，由于巨大的优化空间以及优化对GPU程序性能的复杂不可预测的影响，高质量GPU应用程序的开发具有挑战性。最近的许多努力一直在使用基于经验的基于搜索的自动调谐器来解决该问题，但是很少有人专注于程序输入对优化的影响。在本文中，我们基于一组CUDA和OpenCL内核，报告了一些有关自动调谐器适应程序输入更改重要性的证据，并提出了一种框架G-ADAPT +，以通过构造交叉输入来解决影响预测模型，用于自动预测GPU程序的任意输入的（接近）最佳配置。 G-ADAPT +基于源到源编译器，特别是Cetus和ROSE。它支持CUDA和OpenCL程序的优化。

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