Optimizing function placement for large-scale data-center applications

Modern data-center applications often comprise a large amount of code, with substantial working sets, making them good candidates for code-layout optimizations. Although recent work has evaluated the impact of profile-guided intramodule optimizations and some cross-module optimizations, no recent study has evaluated the benefit of function placement for such large-scale applications. In this paper, we study the impact of function placement in the context of a simple tool we created that uses sample-based profiling data. By using sample-based profiling, this methodology follows the same principle behind AutoFDO, i.e. using profiling data collected from unmodified binaries running in production, which makes it applicable to large-scale binaries. Using this tool, we first evaluate the impact of the traditional Pettis-Hansen (PH) function-placement algorithm on a set of widely deployed data-center applications. Our experiments show that using the PH algorithm improves the performance of the studied applications by an average of 2.6%. In addition to that, this paper also evaluates the impact of two improvements on top of the PH technique. The first improvement is a new algorithm, called C³, which addresses a fundamental weakness we identified in the PH algorithm. We not only qualitatively illustrate how C³ overcomes this weakness in PH, but also present experimental results confirming that C³ performs better than PH in practice, boosting the performance of our workloads by an average of 2.9% on top of PH. The second improvement we evaluate is the selective use of huge pages. Our evaluation shows that, although aggressively mapping the entire code section of a large binary onto huge pages can be detrimental to performance, judiciously using huge pages can further improve performance of our applications by 2.0% on average.