This patch looks good to me. I assume the profile format change in the Android doc will be submitted soon. Since "extbinary" is a superset of "binary", using the "extbinary" format profile in Android shouldn't cause any compatibility issues. Reviewed-by: Rong Xu <xur.google.com> -Rong On Mon, Nov 18, 2024 at 2:25 PM Yabin Cui <yabinc@xxxxxxxxxx> wrote: > > Select ARCH_SUPPORTS_AUTOFDO_CLANG to allow AUTOFDO_CLANG to be > selected. > > On ARM64, ETM traces can be recorded and converted to AutoFDO profiles. > Experiments on Android show 4% improvement in cold app startup time > and 13% improvement in binder benchmarks. > > Signed-off-by: Yabin Cui <yabinc@xxxxxxxxxx> > --- > > Change-Logs in V2: > > 1. Use "For ARM platforms with ETM trace" in autofdo.rst. > 2. Create an issue and a change to use extbinary format in instructions: > https://github.com/Linaro/OpenCSD/issues/65 > https://android-review.googlesource.com/c/platform/system/extras/+/3362107 > > Documentation/dev-tools/autofdo.rst | 18 +++++++++++++++++- > arch/arm64/Kconfig | 1 + > 2 files changed, 18 insertions(+), 1 deletion(-) > > diff --git a/Documentation/dev-tools/autofdo.rst b/Documentation/dev-tools/autofdo.rst > index 1f0a451e9ccd..a890e84a2fdd 100644 > --- a/Documentation/dev-tools/autofdo.rst > +++ b/Documentation/dev-tools/autofdo.rst > @@ -55,7 +55,7 @@ process consists of the following steps: > workload to gather execution frequency data. This data is > collected using hardware sampling, via perf. AutoFDO is most > effective on platforms supporting advanced PMU features like > - LBR on Intel machines. > + LBR on Intel machines, ETM traces on ARM machines. > > #. AutoFDO profile generation: Perf output file is converted to > the AutoFDO profile via offline tools. > @@ -141,6 +141,22 @@ Here is an example workflow for AutoFDO kernel: > > $ perf record --pfm-events RETIRED_TAKEN_BRANCH_INSTRUCTIONS:k -a -N -b -c <count> -o <perf_file> -- <loadtest> > > + - For ARM platforms with ETM trace: > + > + Follow the instructions in the `Linaro OpenCSD document > + https://github.com/Linaro/OpenCSD/blob/master/decoder/tests/auto-fdo/autofdo.md`_ > + to record ETM traces for AutoFDO:: > + > + $ perf record -e cs_etm/@tmc_etr0/k -a -o <etm_perf_file> -- <loadtest> > + $ perf inject -i <etm_perf_file> -o <perf_file> --itrace=i500009il > + > + For ARM platforms running Android, follow the instructions in the > + `Android simpleperf document > + <https://android.googlesource.com/platform/system/extras/+/main/simpleperf/doc/collect_etm_data_for_autofdo.md>`_ > + to record ETM traces for AutoFDO:: > + > + $ simpleperf record -e cs-etm:k -a -o <perf_file> -- <loadtest> > + > 4) (Optional) Download the raw perf file to the host machine. > > 5) To generate an AutoFDO profile, two offline tools are available: > diff --git a/arch/arm64/Kconfig b/arch/arm64/Kconfig > index fd9df6dcc593..c3814df5e391 100644 > --- a/arch/arm64/Kconfig > +++ b/arch/arm64/Kconfig > @@ -103,6 +103,7 @@ config ARM64 > select ARCH_SUPPORTS_PER_VMA_LOCK > select ARCH_SUPPORTS_HUGE_PFNMAP if TRANSPARENT_HUGEPAGE > select ARCH_SUPPORTS_RT > + select ARCH_SUPPORTS_AUTOFDO_CLANG > select ARCH_WANT_BATCHED_UNMAP_TLB_FLUSH > select ARCH_WANT_COMPAT_IPC_PARSE_VERSION if COMPAT > select ARCH_WANT_DEFAULT_BPF_JIT > -- > 2.47.0.338.g60cca15819-goog >
