BOLT is a post-link optimizer developed to speed up large applications. It achieves the improvements by optimizing application's code layout based on execution profile gathered by sampling profiler, such as Linux perf tool. An overview of the ideas implemented in BOLT along with a discussion of its potential and current results is available in CGO'19 paper.
BOLT operates on X86-64 and AArch64 ELF binaries. At the minimum, the binaries should have an unstripped symbol table, and, to get maximum performance gains, they should be linked with relocations (--emit-relocs or -q linker flag).
BOLT disassembles functions and reconstructs the control flow graph (CFG) before it runs optimizations. Since this is a nontrivial task, especially when indirect branches are present, we rely on certain heuristics to accomplish it. These heuristics have been tested on a code generated with Clang and GCC compilers. The main requirement for C/C++ code is not to rely on code layout properties, such as function pointer deltas. Assembly code can be processed too. Requirements for it include a clear separation of code and data, with data objects being placed into data sections/segments. If indirect jumps are used for intra-function control transfer (e.g., jump tables), the code patterns should be matching those generated by Clang/GCC.
NOTE: BOLT is currently incompatible with the -freorder-blocks-and-partition compiler option. Since GCC8 enables this option by default, you have to explicitly disable it by adding -fno-reorder-blocks-and-partition flag if you are compiling with GCC8 or above.
PIE and .so support has been added recently. Please report bugs if you encounter any issues.
You can build and use the docker image containing BOLT using our docker file. Alternatively, you can build BOLT manually using the steps below.
BOLT heavily uses LLVM libraries, and by design, it is built as one of LLVM tools. The build process is not much different from a regular LLVM build. The following instructions are assuming that you are running under Linux.
Start with cloning LLVM repo:
> git clone https://github.com/llvm/llvm-project.git > mkdir build > cd build > cmake -G Ninja ../llvm-project/llvm -DLLVM_TARGETS_TO_BUILD="X86;AArch64" -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=ON -DLLVM_ENABLE_PROJECTS="bolt" > ninja bolt
llvm-bolt will be available under bin/. Add this directory to your path to ensure the rest of the commands in this tutorial work.
BOLT runs many internal passes in parallel. If you foresee heavy usage of BOLT, you can improve the processing time by linking against one of memory allocation libraries with good support for concurrency. E.g. to use jemalloc:
> sudo yum install jemalloc-devel > LD_PRELOAD=/usr/lib64/libjemalloc.so llvm-bolt ....
Or if you rather use tcmalloc:
> sudo yum install gperftools-devel > LD_PRELOAD=/usr/lib64/libtcmalloc_minimal.so llvm-bolt ....
For a complete practical guide of using BOLT see Optimizing Clang with BOLT.
In order to allow BOLT to re-arrange functions (in addition to re-arranging code within functions) in your program, it needs a little help from the linker. Add --emit-relocs to the final link step of your application. You can verify the presence of relocations by checking for .rela.text section in the binary. BOLT will also report if it detects relocations while processing the binary.
This step is different for different kinds of executables. If you can invoke your program to run on a representative input from a command line, then check For Applications section below. If your program typically runs as a server/service, then skip to For Services section.
The version of perf command used for the following steps has to support -F brstack option. We recommend using perf version 4.5 or later.
This assumes you can run your program from a command line with a typical input. In this case, simply prepend the command line invocation with perf:
$ perf record -e cycles:u -j any,u -o perf.data -- <executable> <args> ...
Once you get the service deployed and warmed-up, it is time to collect perf data with LBR (branch information). The exact perf command to use will depend on the service. E.g., to collect the data for all processes running on the server for the next 3 minutes use:
$ perf record -e cycles:u -j any,u -a -o perf.data -- sleep 180
Depending on the application, you may need more samples to be included with your profile. It's hard to tell upfront what would be a sweet spot for your application. We recommend the profile to cover 1B instructions as reported by BOLT -dyno-stats option. If you need to increase the number of samples in the profile, you can either run the sleep command for longer and use -F<N> option with perf to increase sampling frequency.
Note that for profile collection we recommend using cycle events and not BR_INST_RETIRED.*. Empirically we found it to produce better results.
If the collection of a profile with branches is not available, e.g., when you run on a VM or on hardware that does not support it, then you can use only sample events, such as cycles. In this case, the quality of the profile information would not be as good, and performance gains with BOLT are expected to be lower.
If perf record is not available to you, you may collect profile by first instrumenting the binary with BOLT and then running it.
llvm-bolt <executable> -instrument -o <instrumented-executable>
After you run instrumented-executable with the desired workload, its BOLT profile should be ready for you in /tmp/prof.fdata and you can skip Step 2.
Run BOLT with the -help option and check the category “BOLT instrumentation options” for a quick reference on instrumentation knobs.
NOTE: you can skip this step and feed perf.data directly to BOLT using experimental -p perf.data option.
For this step, you will need perf.data file collected from the previous step and a copy of the binary that was running. The binary has to be either unstripped, or should have a symbol table intact (i.e., running strip -g is okay).
Make sure perf is in your PATH, and execute perf2bolt:
$ perf2bolt -p perf.data -o perf.fdata <executable>
This command will aggregate branch data from perf.data and store it in a format that is both more compact and more resilient to binary modifications.
If the profile was collected without LBRs, you will need to add -nl flag to the command line above.
Once you have perf.fdata ready, you can use it for optimizations with BOLT. Assuming your environment is setup to include the right path, execute llvm-bolt:
$ llvm-bolt <executable> -o <executable>.bolt -data=perf.fdata -reorder-blocks=ext-tsp -reorder-functions=hfsort -split-functions -split-all-cold -split-eh -dyno-stats
If you do need an updated debug info, then add -update-debug-sections option to the command above. The processing time will be slightly longer.
For a full list of options see -help/-help-hidden output.
The input binary for this step does not have to 100% match the binary used for profile collection in Step 1. This could happen when you are doing active development, and the source code constantly changes, yet you want to benefit from profile-guided optimizations. However, since the binary is not precisely the same, the profile information could become invalid or stale, and BOLT will report the number of functions with a stale profile. The higher the number, the less performance improvement should be expected. Thus, it is crucial to update .fdata for release branches.
Suppose your application can run in different modes, and you can generate multiple profiles for each one of them. To generate a single binary that can benefit all modes (assuming the profiles don't contradict each other) you can use merge-fdata tool:
$ merge-fdata *.fdata > combined.fdata
Use combined.fdata for Step 3 above to generate a universally optimized binary.
BOLT is licensed under the Apache License v2.0 with LLVM Exceptions.