Feedback-Directed Optimizations in GCC with Estimated Edge Profiles from Hardware Event Sampling
Feedback-Directed Optimizations in GCC with Estimated Edge Profiles from Hardware Event Sampling
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GCC 中的反馈导向优化，通过硬件事件采样估计边缘轮廓
DOI：
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发表时间：
2008
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影响因子：
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通讯作者：
R. Hundt
中科院分区：
文献类型：
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作者：
Vinodha Ramasamy;Paul Yuan;Dehao Chen;R. Hundt
传统反馈指导的优化（FDO）使用静态仪器来收集配置文件。该方法显示出良好的应用程序性能提高，但由于配置文件集合的高运行时间开销，繁琐的双编译使用模型以及生成代表性培训数据集的困难，因此在实践中通常不使用。在本文中，我们表明，可以使用启发式数据来成功构建边缘频率估计，并通过对硬件事件的采样来收集的配置文件，从而产生低运行时开销（例如，少于2％），需要否则仪器，但需要实现竞争性的性能提升。我们的初始结果表明，规格基准的性能增长了3-4％。
Traditional feedback-directed optimization (FDO) uses static instrumentation to collect profiles. This method has shown good application performance gains, but is not commonly used in practice due to the high runtime overhead of profile collection, the tedious dual-compile usage model, and difficulties in generating representative training data sets. In this paper, we show that edge frequency estimates can be successfully constructed with heuristics using profile data collected by sampling of hardware events, incurring low runtime overhead (e.g., less then 2%), and requiring no instrumentation, yet achieving competetive performance gains. Our initial results show a 3-4% performance gain on the SPEC C benchmarks.