Message-ID: <CAF1bQ=R-7z9+57fji4Mn=ZVUgwSniGQ-8H4=42tFunxyp69Wzw@mail.gmail.com>
Date: Wed, 6 Nov 2024 11:00:06 -0800
From: Rong Xu <xur@...gle.com>
To: Masahiro Yamada <masahiroy@...nel.org>
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 Wed, Nov 6, 2024 at 8:09 AM Masahiro Yamada <masahiroy@...nel.org> wrote:
>
> 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.
>

Thanks for taking the patch!

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

We followed the suggestion and removed most of the #if (or #ifdef) in
the linker script.
I just checked: there are two #ifdef remaining:
(1) in the propeller patch for .llvm_bb_addr_map
(2) in linker script patch for arch/sparc/kernel/vmlinux.lds.S.

I think it's likely safe to remove the checks for head_64.o in
non-SPARC64 builds and .llvm_bb_addr_map symbols in non-propeller builds.

SPARC64 builds should always produce head_64.o, and non-SPARC64
builds shouldn't.

Propeller builds always generate .llvm_bb_addr_map symbols, and the
linker will omit the section if it's empty in non-propeller builds.

Keeping the checks is harmless and might slightly reduce linker
workload for matching.
But If you'd prefer to remove them, I'm happy to provide a patch.

Best regards,

-Rong




>
> --
> Best Regards
> Masahiro Yamada

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