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AutoFDO uses sampling based profile to drive feedback directed optimizations.

AutoFDO uses perf to collect sample profiles. A standalone tool is used to convert the perf.data file into gcov format. GCC reads in the gcov file and interprets the profile into a set of hashmaps. A standalone pass is added to use the processed profile data to annotate the basic block counts and estimate branch probabilities.[1]

The major difference between AutoFDO and FDO (aka PGO) is that AutoFDO profiles on optimized binary instead of instrumented binary. This makes it very different in handling cloned functions.


Currently data collection is targeted to Intel CPUs with Last Branch Record hardware registers. [2]

Profile created on x86 can be applied during cross ARM-build though [3] There is a theoretical possibility to use -use_lbr=false flag to record profile on ARM device and a report that it works for linpack benchmark [4]

Also the simplest way to grab a profile is ocperf pmu-tools


Say we have a test-app benchmark, so the AutoFDO build will be the following:

gcc -O2 -g test-app.c -o test-app
ocperf.py record -e br_inst_retired.near_taken:pp -b -o profile.data ./test-app
create_gcov --binary=test-app --profile=profile.data --gcov=test-app.gcov --gcov_version=0x1
gcc -O2 -g test-app.c -o test-app -fauto-profile=test-app.gcov