Halide is a programming language designed to make it easier to write high-performance image and array processing code on modern machines. Halide currently targets:

• CPU architectures: X86, ARM, Hexagon, PowerPC, RISC-V
• Operating systems: Linux, Windows, macOS, Android, iOS, Qualcomm QuRT
• GPU Compute APIs: CUDA, OpenCL, Apple Metal, Microsoft Direct X 12, Vulkan

Rather than being a standalone programming language, Halide is embedded in C++. This means you write C++ code that builds an in-memory representation of a Halide pipeline using Halide's C++ API. You can then compile this representation to an object file, or JIT-compile it and run it in the same process. Halide also provides a Python binding that provides full support for writing Halide embedded in Python without C++.

Halide requires C++17 (or later) to use.

For more detail about what Halide is, see http://halide-lang.org.

For API documentation see http://halide-lang.org/docs

To see some example code, look in the tutorials directory.

If you've acquired a full source distribution and want to build Halide, see the notes below.

The latest version of Halide can always be found on GitHub at https://github.com/halide/Halide/releases

We provide binary releases for many popular platforms and architectures, including 32/64-bit x86 Windows, 64-bit macOS, and 32/64-bit x86/ARM Ubuntu Linux.

If you use vcpkg to manage dependencies, you can install Halide via:

$ vcpkg install halide:x64-windows # or x64-linux/x64-osx

One caveat: vcpkg installs only the minimum Halide backends required to compile code for the active platform. If you want to include all the backends, you should install halide[target-all]:x64-windows instead. Note that since this will build LLVM, it will take a lot of disk space (up to 100GB).

Alternatively, if you use macOS, you can install Halide via Homebrew like so:

$ brew install halide

We are interested in bringing Halide to other popular package managers and Linux distribution repositories including, but not limited to, Conan, Debian, Ubuntu (or PPA), CentOS/Fedora, and Arch. If you have experience publishing packages we would be happy to work with you!

If you are a maintainer of any other package distribution platform, we would be excited to work with you, too.

There are two sets of platform requirements relevant to Halide: those required to run the compiler library in either JIT or AOT mode, and those required to run the binary outputs of the AOT compiler.

These are the tested host toolchain and platform combinations for building and running the Halide compiler library.

Some users have successfully built Halide for Linux using Clang 9.0.0+, for Windows using ClangCL 11.0.0+, and for Windows ARM64 by cross-compiling with MSVC. We do not actively test these scenarios, however, so your mileage may vary.

Beyond these, we are willing to support (by accepting PRs for) platform and toolchain combinations that still receive active, first-party, public support from their original vendors. For instance, at time of writing, this excludes Windows 7 and includes Ubuntu 18.04 LTS.

Compiled AOT pipelines are expected to have much broader platform support. The binaries use the C ABI, and we expect any compliant C compiler to be able to use the generated headers correctly. The C++ bindings currently require C++17. If you discover a compatibility problem with a generated pipeline, please open an issue.

Have llvm-16.0 (or greater) installed and run make in the root directory of the repository (where this README is).

At any point in time, building Halide requires either the latest stable version of LLVM, the previous stable version of LLVM, and trunk. At the time of writing, this means versions 18, 17, and 16 are supported, but 15 is not. The commands llvm-config and clang must be somewhere in the path.

If your OS does not have packages for LLVM, you can find binaries for it at http://llvm.org/releases/download.html. Download an appropriate package and then either install it, or at least put the bin subdirectory in your path. (This works well on OS X and Ubuntu.)

If you want to build it yourself, first check it out from GitHub:

% git clone --depth 1 --branch llvmorg-16.0.6 https://github.com/llvm/llvm-project.git

(If you want to build LLVM 17.x, use branch release/17.x; for current trunk, use main)

Then build it like so:

% cmake -DCMAKE_BUILD_TYPE=Release \
        -DLLVM_ENABLE_PROJECTS="clang;lld;clang-tools-extra" \
        -DLLVM_TARGETS_TO_BUILD="X86;ARM;NVPTX;AArch64;Hexagon;WebAssembly;RISCV" \
        -DLLVM_ENABLE_TERMINFO=OFF -DLLVM_ENABLE_ASSERTIONS=ON \
        -DLLVM_ENABLE_EH=ON -DLLVM_ENABLE_RTTI=ON -DLLVM_BUILD_32_BITS=OFF \
        -DLLVM_ENABLE_RUNTIMES="compiler-rt" \
        -S llvm-project/llvm -B llvm-build
% cmake --build llvm-build
% cmake --install llvm-build --prefix llvm-install

Running a serial build will be slow. To improve speed, try running a parallel build. That's done by default in Ninja; for make, use the option -j NNN, where NNN is the number of parallel jobs, e.g. the number of CPUs you have. Then, point Halide to it:

% export LLVM_ROOT=$PWD/llvm-install
% export LLVM_CONFIG=$LLVM_ROOT/bin/llvm-config

Note that you must add clang to LLVM_ENABLE_PROJECTS; adding lld to LLVM_ENABLE_PROJECTS is only required when using WebAssembly, LLVM_ENABLE_RUNTIMES="compiler-rt" is only required if building the fuzz tests, and adding clang-tools-extra is only necessary if you plan to contribute code to Halide (so that you can run clang-tidy on your pull requests). We recommend enabling both in all cases to simplify builds. You can disable exception handling (EH) and RTTI if you don't want the Python bindings.

With LLVM_CONFIG set (or llvm-config in your path), you should be able to just run make in the root directory of the Halide source tree. make run_tests will run the JIT test suite, and make test_apps will make sure all the apps compile and run (but won't check their output).

There is no make install. If you want to make an install package, use CMake.

If you wish to build Halide in a separate directory, you can do that like so:

% cd ..
% mkdir halide_build
% cd halide_build
% make -f ../Halide/Makefile

Follow the above instructions to build LLVM or acquire a suitable binary release. Then change directory to the Halide repository and run:

% cmake -G Ninja -DCMAKE_BUILD_TYPE=Release -DLLVM_DIR=$LLVM_ROOT/lib/cmake/llvm -S . -B build
% cmake --build build

LLVM_DIR is the folder in the LLVM installation tree (do not use the build tree by mistake) that contains LLVMConfig.cmake. It is not required to set this variable if you have a suitable system-wide version installed. If you have multiple system-wide versions installed, you can specify the version with Halide_REQUIRE_LLVM_VERSION. Remove -G Ninja if you prefer to build with a different generator.

We suggest building with Visual Studio 2019. Your mileage may vary with earlier versions. Be sure to install the "C++ CMake tools for Windows" in the Visual Studio installer. For older versions of Visual Studio, do not install the CMake tools, but instead acquire CMake and Ninja from their respective project websites.

These instructions start from the D: drive. We assume this git repo is cloned to D:\Halide. We also assume that your shell environment is set up correctly. For a 64-bit build, run:

D:\> "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x64

For a 32-bit build, run:

D:\> "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x64_x86

The best way to get compatible dependencies on Windows is to use vcpkg. Install it like so:

D:\> git clone https://github.com/Microsoft/vcpkg.git
D:\> cd vcpkg
D:\> .\bootstrap-vcpkg.bat
D:\vcpkg> .\vcpkg integrate install
...
CMake projects should use: "-DCMAKE_TOOLCHAIN_FILE=D:/vcpkg/scripts/buildsystems/vcpkg.cmake"

Then install the libraries. For a 64-bit build, run:

D:\vcpkg> .\vcpkg install libpng:x64-windows libjpeg-turbo:x64-windows llvm[target-all,clang-tools-extra]:x64-windows

To support 32-bit builds, also run:

D:\vcpkg> .\vcpkg install libpng:x86-windows libjpeg-turbo:x86-windows llvm[target-all,clang-tools-extra]:x86-windows

Create a separate build tree and call CMake with vcpkg's toolchain. This will build in either 32-bit or 64-bit depending on the environment script (vcvars) that was run earlier.

D:\Halide> cmake -G Ninja ^
                 -DCMAKE_BUILD_TYPE=Release ^
                 -DCMAKE_TOOLCHAIN_FILE=D:/vcpkg/scripts/buildsystems/vcpkg.cmake ^
                 -S . -B build

Note: If building with Python bindings on 32-bit (enabled by default), be sure to point CMake to the installation path of a 32-bit Python 3. You can do this by specifying, for example: "-DPython3_ROOT_DIR=C:\Program Files (x86)\Python38-32".

Then run the build with:

D:\Halide> cmake --build build --config Release

To run all the tests:

D:\Halide> cd build
D:\Halide\build> ctest -C Release

Subsets of the tests can be selected with -L and include correctness, python, error, and the other directory names under /tests.

Follow these steps if you want to build LLVM yourself. First, download LLVM's sources (these instructions use the latest 17.0 release)

D:\> git clone --depth 1 --branch release/17.x https://github.com/llvm/llvm-project.git

For a 64-bit build, run:

D:\> cmake -G Ninja ^
           -DCMAKE_BUILD_TYPE=Release ^
           -DLLVM_ENABLE_PROJECTS=clang;lld;clang-tools-extra ^
           -DLLVM_ENABLE_TERMINFO=OFF ^
           -DLLVM_TARGETS_TO_BUILD=X86;ARM;NVPTX;AArch64;Hexagon;RISCV ^
           -DLLVM_ENABLE_ASSERTIONS=ON ^
           -DLLVM_ENABLE_EH=ON ^
           -DLLVM_ENABLE_RTTI=ON ^
           -DLLVM_BUILD_32_BITS=OFF ^
           -S llvm-project\llvm -B llvm-build

For a 32-bit build, run:

D:\> cmake -G Ninja ^
           -DCMAKE_BUILD_TYPE=Release ^
           -DLLVM_ENABLE_PROJECTS=clang;lld;clang-tools-extra ^
           -DLLVM_ENABLE_TERMINFO=OFF ^
           -DLLVM_TARGETS_TO_BUILD=X86;ARM;NVPTX;AArch64;Hexagon;RISCV ^
           -DLLVM_ENABLE_ASSERTIONS=ON ^
           -DLLVM_ENABLE_EH=ON ^
           -DLLVM_ENABLE_RTTI=ON ^
           -DLLVM_BUILD_32_BITS=ON ^
           -S llvm-project\llvm -B llvm32-build

Finally, run:

D:\> cmake --build llvm-build --config Release
D:\> cmake --install llvm-build --prefix llvm-install

You can substitute Debug for Release in the above cmake commands if you want a debug build. Make sure to add -DLLVM_DIR=D:/llvm-install/lib/cmake/llvm to the Halide CMake command to override vcpkg's LLVM.

MSBuild: If you want to build LLVM with MSBuild instead of Ninja, use -G "Visual Studio 16 2019" -Thost=x64 -A x64 or -G "Visual Studio 16 2019" -Thost=x64 -A Win32 in place of -G Ninja.

Do what the build-bots do: https://buildbot.halide-lang.org/master/#/builders

If the column that best matches your system is red, then maybe things aren't just broken for you. If it's green, then you can click the "stdio" links in the latest build to see what commands the build bots run, and what the output was.

HL_TARGET=... will set Halide's AOT compilation target.

HL_JIT_TARGET=... will set Halide's JIT compilation target.

HL_DEBUG_CODEGEN=1 will print out pseudocode for what Halide is compiling. Higher numbers will print more detail.

HL_NUM_THREADS=... specifies the number of threads to create for the thread pool. When the async scheduling directive is used, more threads than this number may be required and thus allocated. A maximum of 256 threads is allowed. (By default, the number of cores on the host is used.)

HL_TRACE_FILE=... specifies a binary target file to dump tracing data into (ignored unless at least one trace_ feature is enabled in HL_TARGET or HL_JIT_TARGET). The output can be parsed programmatically by starting from the code in utils/HalideTraceViz.cpp.

Precompiled Halide distributions are built using XCode's command-line tools with Apple clang 500.2.76. This means that we link against libc++ instead of libstdc++. You may need to adjust compiler options accordingly if you're using an older XCode which does not default to libc++.

Halide supports offloading work to Qualcomm Hexagon DSP on Qualcomm Snapdragon 845/710 devices or newer. The Hexagon DSP provides a set of 128 byte vector instruction extensions - the Hexagon Vector eXtensions (HVX). HVX is well suited for image processing, and Halide for Hexagon HVX will generate the appropriate HVX vector instructions from a program authored in Halide.

Halide can be used to compile Hexagon object files directly, by using a target such as hexagon-32-qurt-hvx.

Halide can also be used to offload parts of a pipeline to Hexagon using the hexagon scheduling directive. To enable the hexagon scheduling directive, include the hvx target feature in your target. The currently supported combination of targets is to use the HVX target features with an x86 linux host (to use the simulator) or with an ARM android target (to use Hexagon DSP hardware). For examples of using the hexagon scheduling directive on both the simulator and a Hexagon DSP, see the blur example app.

To build and run an example app using the Hexagon target,

1. Obtain and build trunk LLVM and Clang. (Earlier versions of LLVM may work but are not actively tested and thus not recommended.)
2. Download and install the Hexagon SDK and Hexagon Tools. Hexagon SDK 4.3.0 or later is needed. Hexagon Tools 8.4 or later is needed.
3. Build and run an example for Hexagon HVX

(Follow the instructions given previously, just be sure to check out the main branch.)

Go to https://qpm.qualcomm.com/#/main/home

1. Go to Tools, and download Qualcomm Package Manager 3. Install the package manager on your machine.
2. Run the installed Qualcomm Package Manager and install the Qualcomm Hexagon SDK 5.x (or 4.x). The SDK can be selected from the Qualcomm Hexagon SDK Products.
3. Set an environment variable to point to the SDK installation location

   export SDK_LOC=/location/of/SDK
   

In addition to running Hexagon code on device, Halide also supports running Hexagon code on the simulator from the Hexagon tools.

To build and run the blur example in Halide/apps/blur on the simulator:

cd apps/blur
export HL_HEXAGON_SIM_REMOTE=../../src/runtime/hexagon_remote/bin/v65/hexagon_sim_remote
export HL_HEXAGON_TOOLS=$SDK_LOC/Hexagon_Tools/8.x/Tools/
LD_LIBRARY_PATH=../../src/runtime/hexagon_remote/bin/host/:$HL_HEXAGON_TOOLS/lib/iss/:. HL_TARGET=host-hvx make test

To build the example for Android, first ensure that you have Android NDK r19b or later installed, and the ANDROID_NDK_ROOT environment variable points to it. (Note that Qualcomm Hexagon SDK v4.3.0 includes Android NDK r19c, which is fine.)

Now build and run the blur example using the script to run it on device:

export HL_HEXAGON_TOOLS=$SDK_LOC/HEXAGON_Tools/8.4.11/Tools/
HL_TARGET=arm-64-android-hvx ./adb_run_on_device.sh