AutoFDO: Enable machine function split optimization for AutoFDO

Enable the machine function split optimization for AutoFDO in Clang.

Machine function split (MFS) is a pass in the Clang compiler tha

AutoFDO: Enable machine function split optimization for AutoFDO

Enable the machine function split optimization for AutoFDO in Clang.

Machine function split (MFS) is a pass in the Clang compiler that
splits a function into hot and cold parts. The linker groups all
cold blocks across functions together. This decreases hot code
fragmentation and improves iCache and iTLB utilization.

MFS requires a profile so this is enabled only for the AutoFDO builds.

Co-developed-by: Han Shen <[email protected]>
Signed-off-by: Han Shen <[email protected]>
Signed-off-by: Rong Xu <[email protected]>
Suggested-by: Sriraman Tallam <[email protected]>
Suggested-by: Krzysztof Pszeniczny <[email protected]>
Tested-by: Yonghong Song <[email protected]>
Tested-by: Yabin Cui <[email protected]>
Tested-by: Nathan Chancellor <[email protected]>
Reviewed-by: Kees Cook <[email protected]>
Signed-off-by: Masahiro Yamada <[email protected]>

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AutoFDO: Enable -ffunction-sections for the AutoFDO build

Enable -ffunction-sections by default for the AutoFDO build.

With -ffunction-sections, the compiler places each function in its own
section

AutoFDO: Enable -ffunction-sections for the AutoFDO build

Enable -ffunction-sections by default for the AutoFDO build.

With -ffunction-sections, the compiler places each function in its own
section named .text.function_name instead of placing all functions in
the .text section. In the AutoFDO build, this allows the linker to
utilize profile information to reorganize functions for improved
utilization of iCache and iTLB.

Co-developed-by: Han Shen <[email protected]>
Signed-off-by: Han Shen <[email protected]>
Signed-off-by: Rong Xu <[email protected]>
Suggested-by: Sriraman Tallam <[email protected]>
Tested-by: Yonghong Song <[email protected]>
Tested-by: Yabin Cui <[email protected]>
Tested-by: Nathan Chancellor <[email protected]>
Reviewed-by: Kees Cook <[email protected]>
Signed-off-by: Masahiro Yamada <[email protected]>

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kbuild: Add AutoFDO support for Clang build

Add the build support for using Clang's AutoFDO. Building the kernel
with AutoFDO does not reduce the optimization level from the
compiler. AutoFDO uses h

kbuild: Add AutoFDO support for Clang build

Add the build support for using Clang's AutoFDO. Building the kernel
with AutoFDO does not reduce the optimization level from the
compiler. AutoFDO uses hardware sampling to gather information about
the frequency of execution of different code paths within a binary.
This information is then used to guide the compiler's optimization
decisions, resulting in a more efficient binary. Experiments
showed that the kernel can improve up to 10% in latency.

The support requires a Clang compiler after LLVM 17. This submission
is limited to x86 platforms that support PMU features like LBR on
Intel machines and AMD Zen3 BRS. Support for SPE on ARM 1,
and BRBE on ARM 1 is part of planned future work.

Here is an example workflow for AutoFDO kernel:

1) Build the kernel on the host machine with LLVM enabled, for example,
$ make menuconfig LLVM=1
Turn on AutoFDO build config:
CONFIG_AUTOFDO_CLANG=y
With a configuration that has LLVM enabled, use the following
command:
scripts/config -e AUTOFDO_CLANG
After getting the config, build with
$ make LLVM=1

2) Install the kernel on the test machine.

3) Run the load tests. The '-c' option in perf specifies the sample
event period. We suggest using a suitable prime number,
like 500009, for this purpose.
For Intel platforms:
$ perf record -e BR_INST_RETIRED.NEAR_TAKEN:k -a -N -b -c <count> \
-o <perf_file> -- <loadtest>
For AMD platforms:
The supported system are: Zen3 with BRS, or Zen4 with amd_lbr_v2
For Zen3:
$ cat proc/cpuinfo | grep " brs"
For Zen4:
$ cat proc/cpuinfo | grep amd_lbr_v2
$ perf record --pfm-events RETIRED_TAKEN_BRANCH_INSTRUCTIONS:k -a \
-N -b -c <count> -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:
create_llvm_prof and llvm_profgen. The create_llvm_prof tool is part
of the AutoFDO project and can be found on GitHub
(https://github.com/google/autofdo), version v0.30.1 or later. The
llvm_profgen tool is included in the LLVM compiler itself. It's
important to note that the version of llvm_profgen doesn't need to
match the version of Clang. It needs to be the LLVM 19 release or
later, or from the LLVM trunk.
$ llvm-profgen --kernel --binary=<vmlinux> --perfdata=<perf_file> \
-o <profile_file>
or
$ create_llvm_prof --binary=<vmlinux> --profile=<perf_file> \
--format=extbinary --out=<profile_file>

Note that multiple AutoFDO profile files can be merged into one via:
$ llvm-profdata merge -o <profile_file> <profile_1> ... <profile_n>

6) Rebuild the kernel using the AutoFDO profile file with the same config
as step 1, (Note CONFIG_AUTOFDO_CLANG needs to be enabled):
$ make LLVM=1 CLANG_AUTOFDO_PROFILE=<profile_file>

Co-developed-by: Han Shen <[email protected]>
Signed-off-by: Han Shen <[email protected]>
Signed-off-by: Rong Xu <[email protected]>
Suggested-by: Sriraman Tallam <[email protected]>
Suggested-by: Krzysztof Pszeniczny <[email protected]>
Suggested-by: Nick Desaulniers <[email protected]>
Suggested-by: Stephane Eranian <[email protected]>
Tested-by: Yonghong Song <[email protected]>
Tested-by: Yabin Cui <[email protected]>
Tested-by: Nathan Chancellor <[email protected]>
Reviewed-by: Kees Cook <[email protected]>
Tested-by: Peter Jung <[email protected]>
Signed-off-by: Masahiro Yamada <[email protected]>

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