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 -o -- + - 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 -- + $ perf inject -i -o --itrace=i500009il + + For ARM platforms running Android, follow the instructions in the + `Android simpleperf document + `_ + to record ETM traces for AutoFDO:: + + $ simpleperf record -e cs-etm:k -a -o -- + 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 8de00bc0a3d3..20281737e325 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