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. An overview of the ideas implemented in BOLT along with a
discussion of its potential and current results is available in `CGO’19
paper <https://research.fb.com/publications/bolt-a-practical-binary-optimizer-for-data-centers-and-beyond/>`__.

Input Binary Requirements
-------------------------

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.

NOTE2: DWARF v5 is the new debugging format generated by the latest LLVM
and GCC compilers. It offers several benefits over the previous DWARF
v4. Currently, the support for v5 is a work in progress for BOLT. While
you will be able to optimize binaries produced by the latest compilers,
until the support is complete, you will not be able to update the debug
info with ``-update-debug-sections``. To temporarily work around the
issue, we recommend compiling binaries with ``-gdwarf-4`` option that
forces DWARF v4 output.

PIE and .so support has been added recently. Please report bugs if you
encounter any issues.

Installation
------------

Docker Image
~~~~~~~~~~~~

You can build and use the docker image containing BOLT using our `docker
file <utils/docker/Dockerfile>`__. Alternatively, you can build BOLT
manually using the steps below.

Manual Build
~~~~~~~~~~~~

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.

Optimizing BOLT’s Performance
-----------------------------

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 ....

Usage
-----

For a complete practical guide of using BOLT see `Optimizing Clang with
BOLT <docs/OptimizingClang.md>`__.

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 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.

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 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.

With instrumentation
^^^^^^^^^^^^^^^^^^^^

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.

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).

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 brstacks, you will need to add `-ba` 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=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.

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.

Profile Formats
---------------

See `Profile Formats <profiles.md>`__ for comprehensive documentation of all
profile formats accepted by BOLT: perf.data, fdata, YAML, and pre-aggregated.

License
-------

BOLT is licensed under the `Apache License v2.0 with LLVM
Exceptions <./LICENSE.TXT>`__.
