|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Halide||5,533||2 days ago||1||February 06, 2020||952||other||C++|
|a language for fast, portable data-parallel computation|
|Highs||634||2 hours ago||1||September 04, 2022||68||mit||C++|
|Linear optimization software|
|Fast Wfc||290||2 years ago||other||C++|
|An implementation of Wave Function Collapse with a focus on performance.|
|Cpp High Performance||194||8 months ago||1||C++|
|C++ High Performance, published by Packt|
|Rajaperf||95||2 days ago||45||bsd-3-clause||Jupyter Notebook|
|RAJA Performance Suite|
|Torchlambda||94||2 years ago||7||mit||Python|
|Lightweight tool to deploy PyTorch models to AWS Lambda|
|Variorum||51||19 days ago||96||mit||C++|
|Vendor-neutral library for exposing power and performance features across diverse architectures|
|Comm_scope||13||2 months ago||14||apache-2.0||C++|
|NUMA-aware multi-CPU multi-GPU data transfer benchmarks|
|Openfec||13||9 years ago||2||other||C|
|Experiments with OpenFEC for OBE|
|Mandelbrot Comparison||10||7 months ago||mit||C++|
|Comparison of Mandelbrot Set programs in different languages with smooth coloring and built-in benchmark mode.|
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:
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 is Halide 13.0.0. 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. See the releases tab on the right (or click here).
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
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.
|GCC||7.5||Ubuntu Linux 20.04 LTS||x86, x64, ARM32|
|GCC||7.5||Ubuntu Linux 18.04 LTS||ARM32, ARM64|
|MSVC||2019 (19.28)||Windows 10 (20H2)||x86, x64|
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-12.0 (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 13.0 and 12.0 are supported, but 11.0 is not. The commands
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-13.0.0 https://github.com/llvm/llvm-project.git
(If you want to build LLVM 12.x, use branch
release/12.x; for current trunk,
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;Mips;Hexagon;WebAssembly" \ -DLLVM_ENABLE_TERMINFO=OFF -DLLVM_ENABLE_ASSERTIONS=ON \ -DLLVM_ENABLE_EH=ON -DLLVM_ENABLE_RTTI=ON -DLLVM_BUILD_32_BITS=OFF \ -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
LLVM_ENABLE_PROJECTS is only required when using WebAssembly, 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.
LLVM_CONFIG set (or
llvm-config in your path), you should be able to
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
-G Ninja if you prefer to build with a
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
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 (
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
error, and the other directory names under
Follow these steps if you want to build LLVM yourself. First, download LLVM's sources (these instructions use the latest 12.0 release)
D:\> git clone --depth 1 --branch llvmorg-13.0.0 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;Mips;Hexagon ^ -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;Mips;Hexagon ^ -DLLVM_ENABLE_ASSERTIONS=ON ^ -DLLVM_ENABLE_EH=ON ^ -DLLVM_ENABLE_RTTI=ON ^ -DLLVM_BUILD_32_BITS=ON ^ -S llvm-project\llvm -B llvm32-build
D:\> cmake --build llvm-build --config Release D:\> cmake --install llvm-build --prefix llvm-install
You can substitute
Release in the above
cmake commands if you
want a debug build. Make sure to add
to the Halide CMake command to override
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
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_JIT_TARGET). The output can be parsed programmatically by starting from the
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
Halide can also be used to offload parts of a pipeline to Hexagon using the
hexagon scheduling directive. To enable the
hexagon scheduling directive,
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,
(Follow the instructions given previously, just be sure to check out the
/location/of/SDK/Hexagon_SDK/4.xand the Hexagon tools into
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