Re: [PATCH v4 0/6] Add AutoFDO and Propeller support for Clang build

[Nathan Chancellor]

Hi Masahiro and Andrew,

Top posting only for visibility. Would it make more sense to have this
land via the Kbuild tree or -mm? The core of the series really touches
Kbuild and I think the x86 stuff can just land with Acks, unless the
-tip folks feel differently. I would like Rong to have a relatively
clear path forward to mainline once the requisite review and testing has
accomplished, which requires a shepherd :)

Cheers,
Nathan

On Mon, Oct 14, 2024 at 02:33:34PM -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.
> 
> 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.  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 patch series adds AutoFDO and Propeller support to the kernel. The
> actual solution comes in six parts:
> 
> [P 1] Add the build support for using AutoFDO in Clang
> 
>       Add the basic support for AutoFDO build and provide the
>       instructions for using AutoFDO.
> 
> [P 2] Fix objtool for bogus warnings when -ffunction-sections is enabled
> 
> [P 3] Change the subsection ordering when -ffunction-sections is enabled
> 
> [P 4] Enable –ffunction-sections for the AutoFDO build
> 
> [P 5] Enable Machine Function Split (MFS) optimization for AutoFDO
> 
> [P 6] Add Propeller configuration to the kernel build
> 
> Patch 1 provides basic AutoFDO build support. Patches 2 to 5 further
> enhance the performance of AutoFDO builds and are functionally dependent
> on Patch 1. Patch 6 enables support for Propeller and is dependent on
> patch 2 and patch 3.
> 
> Caveats
> 
> AutoFDO is compatible with both GCC and Clang, but the patches in this
> series are exclusively applicable to LLVM 17 or newer for AutoFDO and
> LLVM 19 or newer for Propeller. For profile conversion, two different
> tools could be used, llvm_profgen or create_llvm_prof. llvm_profgen
> needs to be the LLVM 19 or newer, or just the LLVM trunk. Alternatively,
> create_llvm_prof v0.30.1 or newer can be used instead of llvm-profgen.
> 
> Additionally, the build is only supported on x86 platforms equipped
> with PMU capabilities, such as LBR on Intel machines. More
> specifically:
>  * Intel platforms: works on every platform that supports LBR;
>    we have tested on Skylake.
>  * AMD platforms: tested on AMD Zen3 with the BRS feature. The kernel
>    needs to be configured with “CONFIG_PERF_EVENTS_AMD_BRS=y", To
>    check, use
>    $ cat /proc/cpuinfo | grep “ brs”
>    For the AMD Zen4, AMD LBRV2 is supported, but we suspect a bug with
>    AMD LBRv2 implementation in Genoa which blocks the usage.
> 
> Experiments and Results
> 
> Experiments were conducted to compare the performance of AutoFDO-optimized
> kernel images (version 6.9.x) against default builds.. The evaluation
> encompassed both open source microbenchmarks and real-world production
> services from Google and Meta. The selected microbenchmarks included Neper,
> a network subsystem benchmark, and UnixBench which is a comprehensive suite
> for assessing various kernel operations.
> 
> For Neper, AutoFDO optimization resulted in a 6.1% increase in throughput
> and a 10.6% reduction in latency. Unixbench saw a 2.2% improvement in its
> index score under low system load and a 2.6% improvement under high system
> load.
> 
> For further details on the improvements observed in Google and Meta's
> production services, please refer to the LLVM discourse post:
> https://discourse.llvm.org/t/optimizing-the-linux-kernel-with-autofdo-including-thinlto-and-propeller/79108
...
> Rong Xu (6):
>   Add AutoFDO support for Clang build
>   objtool: Fix unreachable instruction warnings for weak funcitons
>   Change the symbols order when --ffuntion-sections is enabled
>   AutoFDO: Enable -ffunction-sections for the AutoFDO build
>   AutoFDO: Enable machine function split optimization for AutoFDO
>   Add Propeller configuration for kernel build.
> 
>  Documentation/dev-tools/autofdo.rst   | 165 ++++++++++++++++++++++++++
>  Documentation/dev-tools/index.rst     |   2 +
>  Documentation/dev-tools/propeller.rst | 161 +++++++++++++++++++++++++
>  MAINTAINERS                           |  14 +++
>  Makefile                              |   2 +
>  arch/Kconfig                          |  42 +++++++
>  arch/x86/Kconfig                      |   2 +
>  arch/x86/kernel/vmlinux.lds.S         |   4 +
>  include/asm-generic/vmlinux.lds.h     |  54 +++++++--
>  scripts/Makefile.autofdo              |  25 ++++
>  scripts/Makefile.lib                  |  20 ++++
>  scripts/Makefile.propeller            |  28 +++++
>  tools/objtool/check.c                 |   2 +
>  tools/objtool/elf.c                   |  15 ++-
>  14 files changed, 524 insertions(+), 12 deletions(-)
>  create mode 100644 Documentation/dev-tools/autofdo.rst
>  create mode 100644 Documentation/dev-tools/propeller.rst
>  create mode 100644 scripts/Makefile.autofdo
>  create mode 100644 scripts/Makefile.propeller
> 
> 
> base-commit: eb952c47d154ba2aac794b99c66c3c45eb4cc4ec
> -- 
> 2.47.0.rc1.288.g06298d1525-goog
>