On Sun, Jul 28, 2024 at 01:29:53PM -0700, Rong Xu wrote: > Hi, > > This patch series is to integrate AutoFDO and Propeller support into > the Linux kernel. AutoFDO is a profile-guided optimization technique > that leverages hardware sampling to enhance binary performance. > Unlike Instrumentation-based FDO (iFDO), AutoFDO offers a user-friendly > and straightforward application process. While iFDO generally yields > superior profile quality and performance, our findings reveal that > AutoFDO achieves remarkable effectiveness, bringing performance close > to iFDO for benchmark applications. Similar to AutoFDO, Propeller too > utilizes hardware sampling to collect profiles and apply post-link > optimizations to improve the benchmark’s performance over and above > AutoFDO. > > Our empirical data demonstrates significant performance improvements > with AutoFDO and Propeller, up to 10% on microbenchmarks and up to 5% > on large warehouse-scale benchmarks. This makes a strong case for their > inclusion as supported features in the upstream kernel. > > Background > > A significant fraction of fleet processing cycles (excluding idle time) > from data center workloads are attributable to the kernel. Ware-house > scale workloads maximize performance by optimizing the production kernel > using iFDO (a.k.a instrumented PGO, Profile Guided Optimization). > > iFDO can significantly enhance application performance but its use > within the kernel has raised concerns. AutoFDO is a variant of FDO that > uses the hardware’s Performance Monitoring Unit (PMU) to collect > profiling data. While AutoFDO typically yields smaller performance > gains than iFDO, it presents unique benefits for optimizing kernels. > > AutoFDO eliminates the need for instrumented kernels, allowing a single > optimized kernel to serve both execution and profile collection. It also > minimizes slowdown during profile collection, potentially yielding > higher-fidelity profiling, especially for time-sensitive code, compared > to iFDO. Additionally, AutoFDO profiles can be obtained from production > environments via the hardware’s PMU whereas iFDO profiles require > carefully curated load tests that are representative of real-world > traffic. > > AutoFDO facilitates profile collection across diverse targets. > Preliminary studies indicate significant variation in kernel hot spots > within Google’s infrastructure, suggesting potential performance gains > through target-specific kernel customization. > > Furthermore, other advanced compiler optimization techniques, including > ThinLTO and Propeller can be stacked on top of AutoFDO, similar to iFDO. > ThinLTO achieves better runtime performance through whole-program > analysis and cross module optimizations. The main difference between > traditional LTO and ThinLTO is that the latter is scalable in time and > memory. This, > Propeller is a profile-guided, post-link optimizer that improves > the performance of large-scale applications compiled with LLVM. It > operates by relinking the binary based on an additional round of runtime > profiles, enabling precise optimizations that are not possible at > compile time. should be on top somewhere, not hidden away inside a giant wall of text somewhere at the end.
