Towards Program Optimization through Automated Analysis of Numerical Precision.

Michael D Linderman, Matthew Ho, David L Dill, Teresa H Meng, Garry P Nolan
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引用次数: 57

Abstract

Reducing the arithmetic precision of a computation has real performance implications, including increased speed, decreased power consumption, and a smaller memory footprint. For some architectures, e.g., GPUs, there can be such a large performance difference that using reduced precision is effectively a requirement. The tradeoff is that the accuracy of the computation will be compromised. In this paper we describe a proof assistant and associated static analysis techniques for efficiently bounding numerical and precision-related errors. The programmer/compiler can use these bounds to numerically verify and optimize an application for different input and machine configurations. We present several case study applications that demonstrate the effectiveness of these techniques and the performance benefits that can be achieved with rigorous precision analysis.

Abstract Image

Abstract Image

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通过数值精度自动分析实现程序优化。
降低计算的算术精度具有实际的性能影响,包括提高速度、降低功耗和减少内存占用。对于某些架构,例如gpu,可能存在如此大的性能差异,以至于使用降低的精度实际上是一种要求。这样做的代价是计算的准确性会受到损害。在本文中,我们描述了一个证明辅助和相关的静态分析技术,有效地限制数值和精度相关的误差。程序员/编译器可以使用这些边界在数值上验证和优化应用程序,以适应不同的输入和机器配置。我们提供了几个案例研究应用程序,展示了这些技术的有效性,以及通过严格的精确分析可以实现的性能优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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