On Sun, Oct 20, 2024 at 12:31 PM Nathan Chancellor <nathan@xxxxxxxxxx> wrote: > > 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 :) I think I can pick it up if 2/6 gains Ack from an objtool maintainer. > 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 > > -- Best Regards Masahiro Yamada
