| Message ID | 20240728203001.2551083-1-xur@google.com (mailing list archive) |
|---|---|
| Headers | show |
| Series | Add AutoFDO and Propeller support for Clang build | expand |
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.
On Mon, Jul 29, 2024 at 1:51 AM Peter Zijlstra <peterz@infradead.org> wrote: > > 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. Thanks for the suggestion. I'll move it up. Maybe after the first paragraph in Background. Sorry if you received a duplicated message -- I'm resending this in plain text mode. -Rong
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. 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. 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 Thanks, Rong Xu and Han Shen 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 | 203 ++++++++++++++++++++++++++ Documentation/dev-tools/index.rst | 2 + Documentation/dev-tools/propeller.rst | 188 ++++++++++++++++++++++++ MAINTAINERS | 14 ++ Makefile | 2 + arch/Kconfig | 42 ++++++ arch/x86/Kconfig | 2 + arch/x86/boot/compressed/Makefile | 3 + arch/x86/kernel/vmlinux.lds.S | 4 + arch/x86/platform/efi/Makefile | 1 + drivers/firmware/efi/libstub/Makefile | 2 + include/asm-generic/vmlinux.lds.h | 56 ++++++- scripts/Makefile.autofdo | 22 +++ scripts/Makefile.lib | 20 +++ scripts/Makefile.propeller | 25 ++++ tools/objtool/check.c | 2 + tools/objtool/elf.c | 13 +- 17 files changed, 588 insertions(+), 13 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: 9903efbddba0d14133b5a3c75088b558d2e34ac3