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, MIPS, Hexagon, PowerPC
- Operating systems: Linux, Windows, Mac OS X, Android, iOS, Qualcomm QuRT
- GPU Compute APIs: CUDA, OpenCL, OpenGL, OpenGL Compute Shaders, Apple Metal, Microsoft Direct X 12
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++.
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 10.0.0. 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
Note two caveats: first, at time of writing,
MSVC mis-compiles LLVM on
x86-windows, so Halide cannot be used in vcpkg on that platform at this time;
second, 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 10 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.
Have llvm-9.0 (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 11.0 and 10.0 are supported, but 9.0 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-11.0.0 https://github.com/llvm/llvm-project.git
(If you want to build LLVM 10.x, use branch llvmorg-10.0.1; for current trunk,
use master)
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" \
-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
then to 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, 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 yet. If you want to make an install package, run
make distrib.
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 -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. Add -G Ninja if you prefer to build with the
Ninja 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 -j %NUMBER_OF_PROCESSORS%
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 11.0 release)
D:\> git clone --depth 1 --branch llvmorg-11.0.0 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
Finally, run:
D:\> cmake --build llvm-build --config Release -j %NUMBER_OF_PROCESSORS%
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's OpenGL backend offloads image processing operations to the GPU by generating GLSL-based fragment shaders.
Compared to other GPU-based processing options such as CUDA and OpenCL, OpenGL has two main advantages: it is available on basically every desktop computer and mobile device, and it is generally well supported across different hardware vendors.
The main disadvantage of OpenGL as an image processing framework is that the computational capabilities of fragment shaders are quite restricted. In general, the processing model provided by OpenGL is most suitable for filters where each output pixel can be expressed as a simple function of the input pixels. This covers a wide range of interesting operations like point-wise filters and convolutions; but a few common image processing operations such as histograms or recursive filters are notoriously hard to express in GLSL.
To enable code generation for OpenGL, include opengl in the target specifier
passed to Halide. Since OpenGL shaders are limited in their computational power,
you must also specify a CPU target for those parts of the filter that cannot or
should not be computed on the GPU. Examples of valid target specifiers are
host-opengl
x86-opengl-debug
Adding debug, as in the second example, adds additional logging output and is
highly recommended during development.
By default, filters compiled for OpenGL targets run completely on the CPU. Execution on the GPU must be enabled for individual Funcs by appropriate scheduling calls.
GLSL fragment shaders implicitly iterate over two spatial dimensions x,y and the color channel. Due to the way color channels handled in GLSL, only filters for which the color index is a compile-time constant can be scheduled. The main consequence is that the range of color variables must be explicitly specified for both input and output buffers before scheduling:
ImageParam input;
Func f;
Var x, y, c;
f(x, y, c) = ...;
input.set_bounds(2, 0, 3); // specify color range for input
f.bound(c, 0, 3); // and output
f.glsl(x, y, c);
For JIT compilation Halide attempts to load the system libraries for opengl and creates a new context to use for each module. Windows is not yet supported.
Examples for JIT execution of OpenGL-based filters can be found in test/opengl.
When AOT (ahead-of-time) compilation is used, Halide generates OpenGL-enabled object files that can be linked to and called from a host application. In general, this is fairly straightforward, but a few things must be taken care of.
On Linux, OS X, and Android, Halide creates its own OpenGL context unless the current thread already has an active context. On other platforms you have to link implementations of the following two functions with your Halide code:
extern "C" int halide_opengl_create_context(void *) {
return 0; // if successful
}
extern "C" void *halide_opengl_get_proc_addr(void *, const char *name) {
...
}
Halide allocates and deletes textures as necessary. Applications may manage the
textures by hand by setting the halide_buffer_t::device field; this is most
useful for reusing image data that is already stored in textures. Some
rudimentary checks are performed to ensure that externally allocated textures
have the correct format, but in general that's the responsibility of the
application.
It is possible to let render directly to the current framebuffer; to do this,
set the dev field of the output buffer to the value returned by
halide_opengl_output_client_bound. The example in apps/HelloAndroidGL
demonstrates this technique.
Some operating systems can delete the OpenGL context of suspended applications.
If this happens, Halide needs to re-initialize itself with the new context after
the application resumes. Call halide_opengl_context_lost to reset Halide's
OpenGL state after this has happened.
The current implementation of the OpenGL backend targets the common subset of OpenGL 2.0 and OpenGL ES 2.0 which is widely available on both mobile devices and traditional computers. As a consequence, only a subset of the Halide language can be scheduled to run using OpenGL. Some important limitations are:
-
Reductions cannot be implemented in GLSL and must be run on the CPU.
-
OpenGL ES 2.0 only supports uint8 buffers.
Support for floating point texture is available, but requires OpenGL (ES) 3.0 or the texture_float extension, which may not work on all mobile devices.
-
OpenGL ES 2.0 has very limited support for integer arithmetic. For maximum compatibility, consider doing all computations using floating point, even when using integer textures.
-
Only 2D images with 3 or 4 color channels can be scheduled. Images with one or two channels require OpenGL (ES) 3.0 or the texture_rg extension.
-
Not all builtin functions provided by Halide are currently supported, for example
fast_log,fast_exp,fast_pow,reinterpret, bit operations,random_float,random_intcannot be used in GLSL code.
The maximum texture size in OpenGL is GL_MAX_TEXTURE_SIZE, which is often
smaller than the image of interest; on mobile devices, for example,
GL_MAX_TEXTURE_SIZE is commonly 2048. Tiling must be used to process larger
images.
Planned features:
-
Support for half-float textures and arithmetic
-
Support for integer textures and arithmetic
(Note that OpenGL Compute Shaders are supported with a separate OpenGLCompute backend.)
Halide supports offloading work to Qualcomm Hexagon DSP on Qualcomm Snapdragon 835 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,
- Obtain and build trunk LLVM and Clang. (Earlier versions of LLVM may work but are not actively tested and thus not recommended.)
- Download and install the Hexagon SDK and Hexagon Tools. Hexagon SDK 3.4.1 or later is needed. Hexagon Tools 8.2 or later is needed.
- Build and run an example for Hexagon HVX
(Instructions given previous, just be sure to check out the master branch.)
Go to https://developer.qualcomm.com/software/hexagon-dsp-sdk/tools
- Select the Hexagon Series 600 Software and download the 3.4.1 version or later for Linux.
- untar the installer
- Run the extracted installer to install the Hexagon SDK and Hexagon Tools,
selecting Installation of Hexagon SDK into
/location/of/SDK/Hexagon_SDK/3.xand the Hexagon tools into/location/of/SDK/Hexagon_Tools/8.x - 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/v62/hexagon_sim_remote
export HL_HEXAGON_TOOLS=$SDK_LOC/Hexagon_Tools/8.x/Tools/
LD_LIBRARY_PATH=../../src/runtime/hexagon_remote/bin/host/:$HL_HEXAGON_TOOLS/lib/iss/:. HL_TARGET=host-hvx make test
To build the example for Android, first ensure that you have Android NDK r19b or later installed, and the ANDROID_NDK_ROOT environment variable points to it. (Note that Qualcomm Hexagon SDK v3.5.2 includes Android NDK r19c, which is fine.)
Now build and run the blur example using the script to run it on device:
export HL_HEXAGON_TOOLS=$SDK_LOC/HEXAGON_Tools/8.0/Tools/
HL_TARGET=arm-64-android-hvx ./adb_run_on_device.sh