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Diffstat (limited to 'Documentation/dev-tools')
| -rw-r--r-- | Documentation/dev-tools/autofdo.rst | 41 |
1 files changed, 41 insertions, 0 deletions
diff --git a/Documentation/dev-tools/autofdo.rst b/Documentation/dev-tools/autofdo.rst index bcf06e7d6ffa..ae03c4dfedc1 100644 --- a/Documentation/dev-tools/autofdo.rst +++ b/Documentation/dev-tools/autofdo.rst @@ -61,6 +61,9 @@ process consists of the following steps: the AutoFDO profile via offline tools. The support requires a Clang compiler LLVM 17 or later. +Current supported architectures include x86/x86_64 (via LBR) and +arm64 (via SPE or ETM). + Preparation =========== @@ -141,6 +144,35 @@ 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 arm64 with SPE: + + There are a few kernel features that must be enabled to collect SPE profiles on Arm. + Below is a list of the required features: + + - CONFIG_ARM_SPE_PMU=y + - CONFIG_PID_IN_CONTEXTIDR=y + - kpti=off + + Use the following command to generate SPE perf data file:: + + $ perf record -e ' arm_spe_0/branch_filter=1,load_filter=0,store_filter=0/' -a -c <count> -N --no-switch-events -o <perf_file> -- <loadtest> + + - For arm64 with ETM trace: + + Follow the instructions in `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 `Android simpleperf + document <https://android.googlesource.com/kernel/common/+/refs/heads/android-mainline/gki/aarch64/afdo>`_ + to record ETM traces for AutoFDO:: + + $ simpleperf record -e cs-etm:k -a -o <etm_perf_file> -- <loadtest> + $ simpleperf inject -i <etm_perf_file> -o <text_perf_file> --symdir <vmlinux_dir> + 4) (Optional) Download the raw perf file to the host machine. 5) To generate an AutoFDO profile, two offline tools are available: @@ -162,6 +194,15 @@ Here is an example workflow for AutoFDO kernel: $ llvm-profdata merge -o <profile_file> <profile_1> <profile_2> ... <profile_n> + For arm64 SPE, use the following command:: + + $ create_llvm_prof --binary=<vmlinux> --profile=<perf_file> --profiler=perf_spe --format=extbinary --out=<profile_file> + + For arm64 ETM, use the following command:: + + $ create_llvm_prof --binary=<vmlinux> --profile=<text_perf_file> --profiler=text -format=extbinary -out=<profile_file> + + 6) Rebuild the kernel using the AutoFDO profile file with the same config as step 1, (Note CONFIG_AUTOFDO_CLANG needs to be enabled):: |