Revamping Sampling-Based PGO with Context-Sensitivity and Pseudo-instrumentation

• Main Authors: He, Wenlei, Yu, Hongtao, Wang, Lei, Oh, Taewook
• Format: Conference Proceeding
• Language: English
• Subjects: Compiler; Context-sensitive Profiling; Data centers; Feedback Directed Optimization; Instrumentation; Instruments; Optimization; Production; Profile Guided Optimization; Sampling; Scalability; Servers; Usability
• ISSN: 2643-2838
• DOI: 10.1109/CGO57630.2024.10444807

The ever increasing scale of modern data center demands more effective optimizations, as even a small percentage of performance improvement can result in a significant reduction in data-center cost and its environmental footprint. However, the diverse set of workloads running in data centers also challenges the scalability of optimization solutions. Profile-guided optimization (PGO) is a promising technique to improve application performance. Sampling-based PGO is widely used in data-center applications due to its low operational overhead, but the performance gains are not as substantial as the instrumentation-based counterpart. The high operational overhead of instrumentation-based PGO, on the other hand, hinders its large-scale adoption, despite its superior performance gains. In this paper, we propose CSSPGO, a context-sensitive sampling-based PGO framework with pseudo-instrumentation. CSSPGO offers a more balanced solution to push sampling-based PGO performance closer to instrumentation-based PGO while maintaining minimal operational overhead. It leverages pseudo-instrumentation to improve profile quality without incurring the overhead of traditional instrumentation. It also enriches profile with context-sensitivity to aid more effective optimizations through a novel profiling methodology using synchronized LBR and stack sampling. CSSPGO is now used to optimize over 75% of Meta's data center CPU cycles. Our evaluation with production workloads demonstrates 1%-5% performance improvement on top of state-of-the-art sampling-based PGO.