Taming hardware event samples for FDO compilation

Feedback-directed optimization (FDO) is effective in improving application runtime performance, but has not been widely adopted due to the tedious dual-compilation model, the difficulties in generating representative training data sets, and the high runtime overhead of profile collection. The use of hardware-event sampling to generate estimated edge profiles overcomes these drawbacks. Yet, hardware event samples are typically not precise at the instruction or basic-block granularity. These inaccuracies lead to missed performance when compared to instrumentation-based FDO. In this paper, we use multiple hardware event profiles and supervised learning techniques to generate heuristics for improved precision of basic-block-level sample profiles, and to further improve the smoothing algorithms used to construct edge profiles. We demonstrate that sampling-based FDO can achieve an average of 78% of the performance gains obtained using instrumentation-based exact edge profiles for SPE...

• Type: Conference
• Year: 2010
• Where: CGO
• Authors: Dehao Chen, Neil Vachharajani, Robert Hundt, Shih-wei Liao, Vinodha Ramasamy, Paul Yuan, Wenguang Chen, Weimin Zheng
• Keywords: CGO 2010, Edge Profiles, Feedback-directed Optimization, PERFORMANCE OF SYSTEMS, Software Engineering