BOLT

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. BOLT can operate on any binary with a symbol table, but for maximum gains it utilizes relocations saved by a linker (--emit-relocs).

NOTE: current support is limited to non-PIE X86-64 and AArch64 ELF binaries.

INSTALLATION

BOLT heavily uses LLVM libraries and by design it is built as one of LLVM tools. The build process in not much different from a regular LLVM build. The following instructions are assuming that you are running under Linux.

Start with cloning LLVM and BOLT repos:

> git clone https://github.com/llvm-mirror/llvm llvm
> cd llvm/tools
> git checkout -b llvm-bolt f137ed238db11440f03083b1c88b7ffc0f4af65e
> git clone https://github.com/facebookincubator/BOLT llvm-bolt
> cd ..
> patch -p 1 < tools/llvm-bolt/llvm.patch

Proceed to a normal LLVM build:

> cd ..
> mkdir build
> cd build
> cmake -G Ninja ../llvm
> ninja

llvm-bolt will be available under bin/. Add this directory to your path to ensure the rest of the commands in this tutorial work.

Note that we use a specific revision of LLVM as we currently rely on a set of patches that are not yet upstreamed.

USAGE

Step 0

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.

Step 1: Collect Profile

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 programs typically runs as a server/service, then skip to For Services section.

For Applications

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> ...

For Services

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/or 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 collection of a profile with branches is not available, e.g. when you run on a VM or on a 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.

Step 2: Convert Profile to BOLT Format

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).

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.

Step 3: Optimize with BOLT

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=cache+ -reorder-functions=hfsort+ -split-functions=3 -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 an active development, and the source code constantly changes, yet you want to benefit from profile-guided optimizations. However, since the binary is not exactly the same, the profile information could become invalid or stale, and BOLT will report the number of functions with stale profile. The higher the number, the less performance improvement should be expected. Thus, it is important to update .fdata for important releases.

Multiple Profiles

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.

LlsDimple/BOLT