使用元启发式搜索技术寻找弹性友好的编译器优化

Nithya Narayanamurthy, K. Pattabiraman, M. Ripeanu
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引用次数: 21

摘要

随着预计未来硬件错误率的增加,应用软件需要对硬件故障具有弹性。影响应用程序的错误弹性和脆弱性的一个重要因素是编译时使用的优化集。我们提出了一种基于遗传算法的自动化技术,以找到特定于应用程序的编译器优化集,这些优化集可以在不降低应用程序的错误恢复能力的情况下提高性能。我们发现,与使用标准优化级别(即O1、O2、O3)编译时相比,优化后的代码具有明显更好的错误恢复能力,同时获得了相当的性能改进,从而降低了整体漏洞。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Finding Resilience-Friendly Compiler Optimizations Using Meta-Heuristic Search Techniques
With the projected future increase in hardware error rates, application software needs to be resilient to hardware faults. An important factor affecting an application's error resilience and vulnerability is the set of optimizations used when compiling it. We propose an automated technique based on genetic algorithms to find the application-specific set of compiler optimizations that can boost performance without degrading the application's error resilience. We find that the resulting optimized code has significantly better error resilience than when being compiled with the standard optimization levels (i.e., O1, O2, O3), while attaining comparable performance improvements, thus leading to lower overall vulnerabilities.
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