How To Build Clang and LLVM with Profile-Guided Optimizations

Introduction

PGO (Profile-Guided Optimization) allows your compiler to better optimize code for how it actually runs. Users report that applying this to Clang and LLVM can decrease overall compile time by 20%.

This guide walks you through how to build Clang with PGO, though it also applies to other subprojects, such as LLD.

Using the script

We have a script at utils/collect_and_build_with_pgo.py. This script is tested on a few Linux flavors, and requires a checkout of LLVM, Clang, and compiler-rt. Despite the the name, it performs four clean builds of Clang, so it can take a while to run to completion. Please see the script’s --help for more information on how to run it, and the different options available to you. If you want to get the most out of PGO for a particular use-case (e.g. compiling a specific large piece of software), please do read the section below on ‘benchmark’ selection.

Please note that this script is only tested on a few Linux distros. Patches to add support for other platforms, as always, are highly appreciated. :)

This script also supports a --dry-run option, which causes it to print important commands instead of running them.

Selecting ‘benchmarks’

PGO does best when the profiles gathered represent how the user plans to use the compiler. Notably, highly accurate profiles of llc building x86_64 code aren’t incredibly helpful if you’re going to be targeting ARM.

By default, the script above does two things to get solid coverage. It:

  • runs all of Clang and LLVM’s lit tests, and
  • uses the instrumented Clang to build Clang, LLVM, and all of the other LLVM subprojects available to it.

Together, these should give you:

  • solid coverage of building C++,
  • good coverage of building C,
  • great coverage of running optimizations,
  • great coverage of the backend for your host’s architecture, and
  • some coverage of other architectures (if other arches are supported backends).

Altogether, this should cover a diverse set of uses for Clang and LLVM. If you have very specific needs (e.g. your compiler is meant to compile a large browser for four different platforms, or similar), you may want to do something else. This is configurable in the script itself.

Building Clang with PGO

If you prefer to not use the script, this briefly goes over how to build Clang/LLVM with PGO.

First, you should have at least LLVM, Clang, and compiler-rt checked out locally.

Next, at a high level, you’re going to need to do the following:

  1. Build a standard Release Clang and the relevant libclang_rt.profile library
  2. Build Clang using the Clang you built above, but with instrumentation
  3. Use the instrumented Clang to generate profiles, which consists of two steps:
  • Running the instrumented Clang/LLVM/lld/etc. on tasks that represent how users will use said tools.
  • Using a tool to convert the “raw” profiles generated above into a single, final PGO profile.
  1. Build a final release Clang (along with whatever other binaries you need) using the profile collected from your benchmark

In more detailed steps:

  1. Configure a Clang build as you normally would. It’s highly recommended that you use the Release configuration for this, since it will be used to build another Clang. Because you need Clang and supporting libraries, you’ll want to build the all target (e.g. ninja all or make -j4 all).
  2. Configure a Clang build as above, but add the following CMake args:
    • -DLLVM_BUILD_INSTRUMENTED=IR – This causes us to build everything with instrumentation.
    • -DLLVM_BUILD_RUNTIME=No – A few projects have bad interactions when built with profiling, and aren’t necessary to build. This flag turns them off.
    • -DCMAKE_C_COMPILER=/path/to/stage1/clang - Use the Clang we built in step 1.
    • -DCMAKE_CXX_COMPILER=/path/to/stage1/clang++ - Same as above.
In this build directory, you simply need to build the clang target (and whatever supporting tooling your benchmark requires).

  1. As mentioned above, this has two steps: gathering profile data, and then massaging it into a useful form:

    1. Build your benchmark using the Clang generated in step 2. The ‘standard’ benchmark recommended is to run check-clang and check-llvm in your instrumented Clang’s build directory, and to do a full build of Clang/LLVM using your instrumented Clang. So, create yet another build directory, with the following CMake arguments:

      • -DCMAKE_C_COMPILER=/path/to/stage2/clang - Use the Clang we built in step 2.
      • -DCMAKE_CXX_COMPILER=/path/to/stage2/clang++ - Same as above.

      If your users are fans of debug info, you may want to consider using -DCMAKE_BUILD_TYPE=RelWithDebInfo instead of -DCMAKE_BUILD_TYPE=Release. This will grant better coverage of debug info pieces of clang, but will take longer to complete and will result in a much larger build directory.

      It’s recommended to build the all target with your instrumented Clang, since more coverage is often better.

  1. You should now have a few *.profraw files in path/to/stage2/profiles/. You need to merge these using llvm-profdata (even if you only have one! The profile merge transforms profraw into actual profile data, as well). This can be done with /path/to/stage1/llvm-profdata merge -output=/path/to/output/profdata.prof path/to/stage2/profiles/*.profraw.

  1. Now, build your final, PGO-optimized Clang. To do this, you’ll want to pass the following additional arguments to CMake.

    • -DLLVM_PROFDATA_FILE=/path/to/output/profdata.prof - Use the PGO profile from the previous step.
    • -DCMAKE_C_COMPILER=/path/to/stage1/clang - Use the Clang we built in step 1.
    • -DCMAKE_CXX_COMPILER=/path/to/stage1/clang++ - Same as above.

    From here, you can build whatever targets you need.

    Note

    You may see warnings about a mismatched profile in the build output. These are generally harmless. To silence them, you can add -DCMAKE_C_FLAGS='-Wno-backend-plugin' -DCMAKE_CXX_FLAGS='-Wno-backend-plugin' to your CMake invocation.

Congrats! You now have a Clang built with profile-guided optimizations, and you can delete all but the final build directory if you’d like.

If this worked well for you and you plan on doing it often, there’s a slight optimization that can be made: LLVM and Clang have a tool called tblgen that’s built and run during the build process. While it’s potentially nice to build this for coverage as part of step 3, none of your other builds should benefit from building it. You can pass the CMake options -DCLANG_TABLEGEN=/path/to/stage1/bin/clang-tblgen -DLLVM_TABLEGEN=/path/to/stage1/bin/llvm-tblgen to steps 2 and onward to avoid these useless rebuilds.