Message-ID: <CAK7LNASdBPtq4vaK0XZQvxicOY15qJFsnqkO2_us4AU4ppHw6A@mail.gmail.com>
Date: Thu, 7 Nov 2024 01:08:40 +0900
From: Masahiro Yamada <masahiroy@...nel.org>
To: Rong Xu <xur@...gle.com>
Cc: Alice Ryhl <aliceryhl@...gle.com>, Andrew Morton <akpm@...ux-foundation.org>, 
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Subject: Re: [PATCH v7 0/7] Add AutoFDO and Propeller support for Clang build

On Sun, Nov 3, 2024 at 2:51 AM Rong Xu <xur@...gle.com> 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] Adjust symbol ordering in text output sections
>
> [P 4] Add markers for text_unlikely and text_hot sections
>
> [P 5] Enable –ffunction-sections for the AutoFDO build
>
> [P 6] Enable Machine Function Split (MFS) optimization for AutoFDO
>
> [P 7] Add Propeller configuration to the kernel build
>
> Patch 1 provides basic AutoFDO build support. Patches 2 to 6 further
> enhance the performance of AutoFDO builds and are functionally dependent
> on Patch 1. Patch 7 enables support for Propeller and is dependent on
> patch 2 to patch 4.
>
> 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.
>
> For ARM, we plan to send patches for SPE-based Propeller when
> AutoFDO for Arm is ready.
>
> 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
>
> Thanks,
>
> Rong Xu and Han Shen


I applied this series to linux-kbuild.

As I mentioned before, I do not like #ifdef because
it hides (not fixes) issues only for default cases.


-- 
Best Regards
Masahiro Yamada

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