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mold: A Modern Linker

mold is a faster drop-in replacement for existing Unix linkers. It is several times faster than LLVM lld linker, the second-fastest open-source linker which I originally created a few years ago. mold is created for increasing developer productivity by reducing build time especially in rapid debug-edit-rebuild cycles.

Here is a performance comparison of GNU gold, LLVM lld, and mold for linking final debuginfo-enabled executables of major large programs on a simulated 8-core 16-threads machine.

Link speed comparison

Program (linker output size) GNU gold LLVM lld mold
Chrome 96 (1.89 GiB) 53.86s 11.74s 2.21s
Clang 13 (3.18 GiB) 64.12s 5.82s 2.90s
Firefox 89 libxul (1.64 GiB) 32.95s 6.80s 1.42s

mold is so fast that it is only 2x slower than cp on the same machine.

Feel free to file a bug if you find mold is not faster than other linkers.

Why does the speed of linking matter?

If you are using a compiled language such as C, C++ or Rust, a build consists of two phases. In the first phase, a compiler compiles a source file into an object file (.o files). In the second phase, a linker takes all object files to combine them into a single executable or a shared library file.

The second phase takes a long time if your build output is large. mold can make it faster, saving your time and keeping you from being distracted while waiting for a long build to finish. The difference is most noticeable when you are in rapid debug-edit-rebuild cycles.

Install

Binary packages for the following distros are currently available.

Packaging status

How to build

mold is written in C++20, so if you build mold yourself, you need a very recent version of GCC or Clang. I'm using Ubuntu 20.04 as a development platform. In that environment, you can build mold by the following commands.

Install dependencies

Ubuntu 20.04 and later / Debian 11 and later

sudo apt-get update
sudo apt-get install -y build-essential git clang cmake libstdc++-10-dev libssl-dev libxxhash-dev zlib1g-dev

Fedora 34 and later

sudo dnf install -y git clang cmake openssl-devel xxhash-devel zlib-devel libstdc++-devel

Compile mold

git clone https://github.com/rui314/mold.git
cd mold
git checkout v1.0.1
make -j$(nproc)
sudo make install

By default, mold is installed to /usr/local/bin.

If you don't use a recent enough Linux distribution, or if for any reason make in the above commands doesn't work for you, you can use Docker to build it in a Docker environment. To do so, just run ./build-static.sh in this directory instead of running make -j$(nproc). The shell script creates a Ubuntu 20.04 Docker image, installs necessary tools and libraries to it, and builds mold as a statically-linked executable.

make test depends on a few more packages. To install, run the following commands:

sudo dpkg --add-architecture i386
sudo apt update
sudo apt-get install bsdmainutils dwarfdump libc6-dev:i386 lib32gcc-10-dev libstdc++-10-dev-arm64-cross gcc-10-aarch64-linux-gnu g++-10-aarch64-linux-gnu

How to use

A classic way to use mold

On Unix, the linker command (which is usually /usr/bin/ld) is invoked indirectly by the compiler driver (which is usually cc, gcc or clang), which is typically in turn indirectly invoked by make or some other build system command.

If you can specify an additional command line option to your compiler driver by modifying build system's config files, add one of the following flags to use mold instead of /usr/bin/ld:

  • Clang: pass -fuse-ld=mold

  • GCC 12.1.0 or later: pass -fuse-ld=mold

  • GCC before 12.1.0: -fuse-ld does not accept mold as a valid argument, so you need to use -B option instead. -B is an option to tell GCC where to look for external commands such as ld.

    If you have installed mold with make install, there should be a directory named /usr/libexec/mold (or /usr/local/libexec/mold, depending on your $PREFIX), and ld command should be there. The ld is actually a symlink to mold. So, all you need is to pass -B/usr/libexec/mold (or -B/usr/local/libexec/mold) to GCC.

If you haven't installed mold to any $PATH, you can still pass -fuse-ld=/absolute/path/to/mold to clang to use mold. GCC does not take an absolute path as an argument for -fuse-ld though.

If you are using Rust

Create .cargo/config.toml in your project directory with the following:

[target.x86_64-unknown-linux-gnu]
linker = "clang"
rustflags = ["-C", "link-arg=-fuse-ld=/path/to/mold"]

where /path/to/mold is an absolute path to mold exectuable.

If you want to use mold for all projects, put the above snippet to ~/.cargo/config.toml.

mold -run

It is sometimes very hard to pass an appropriate command line option to cc to specify an alternative linker. To deal with the situation, mold has a feature to intercept all invocations of ld, ld.lld or ld.gold and redirect it to itself. To use the feature, run make (or another build command) as a subcommand of mold as follows:

mold -run make <make-options-if-any>

Here's an example showing how to link Rust code when using the cargo package manager:

mold -run cargo build

Internally, mold invokes a given command with LD_PRELOAD environment variable set to its companion shared object file. The shared object file intercepts all function calls to exec(3)-family functions to replace argv[0] with mold if it is ld, ld.gold or ld.lld.

mold leaves its identification string in .comment section in an output file. You can print it out to verify that you are actually using mold.

readelf -p .comment <executable-file>

String dump of section '.comment':
  [     0]  GCC: (Ubuntu 10.2.0-5ubuntu1~20.04) 10.2.0
  [    2b]  mold 9a1679b47d9b22012ec7dfbda97c8983956716f7

If mold is in .comment, the file is created by mold.

Why is mold so fast?

One reason is because it simply uses faster algorithms and efficient data structures than other linkers do. The other reason is that the new linker is highly parallelized.

Here is a side-by-side comparison of per-core CPU usage of lld (left) and mold (right). They are linking the same program, Chromium executable.

CPU usage comparison in htop animation

As you can see, mold uses all available cores throughout its execution and finishes quickly. On the other hand, lld failed to use available cores most of the time. In this demo, the maximum parallelism is artificially capped to 16 so that the bars fit in the GIF.

For details, please read design notes.

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