Re: [PATCH 0/6] Add AutoFDO and Propeller support for Clang build

[Peter Zijlstra <peterz@infradead.org>]

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.