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a language for fast, portable data-parallel computation
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Halide is a programming language designed to make it easier to write high-performance image 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.

For more detail about what Halide is, see

For API documentation see

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.

Build Status

linux build status

Building Halide


Have llvm-6.0 or greater installed and run make in the root directory of the repository (where this README is).

Acquiring LLVM

Building Halide requires at least LLVM 6.0, along with the matching version of Clang. llvm-config and clang must be somewhere in the path. If your OS does not have packages for llvm-6.0, you can find binaries for it at 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 subversion:

% svn co llvm6.0
% svn co llvm6.0/tools/clang

Then build it like so:

% cd llvm6.0
% mkdir build
% cd build
% make -j8

then to point Halide to it:

export LLVM_CONFIG=<path to llvm>/build/bin/llvm-config
export CLANG=<path to llvm>/build/bin/clang

Building Halide with make

With LLVM_CONFIG and CLANG set (or llvm-config and clang 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.

Building Halide out-of-tree with make

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

Building Halide with cmake

If you wish to use cmake to build Halide, the build procedure is:

% mkdir cmake_build
% cd cmake_build
% cmake -DLLVM_DIR=/path-to-llvm-build/lib/cmake/llvm -DCMAKE_BUILD_TYPE=Release /path/to/halide
% make -j8

LLVM_DIR should be the folder in the LLVM installation or build tree that contains LLVMConfig.cmake.

Building Halide and LLVM on Windows

Acquire MSVC 2015 Update 3 or newer. Earlier versions may work but are not part of our tests. MSBuild and cmake should also be in your path. The instructions below assume Halide is checked out under C:\Code\Halide, and LLVM and Clang are checked out under C:\Code\llvm.

% mkdir C:\Code\llvm-build
% cd C:\Code\llvm-build

For a 32-bit build use:


Then build it like so:

% MSBuild.exe /m /t:Build /p:Configuration=Release .\INSTALL.vcxproj

You can substitute Debug for Release in both commands if you want a debug build.

To configure and build Halide:

% mkdir C:\Code\halide-build
% cd C:\Code\halide-build
% cmake -DLLVM_DIR=../llvm-install/lib/cmake/llvm -DCMAKE_BUILD_TYPE=Release -G "Visual Studio 14 Win64" ../halide
% MSBuild.exe /m /t:Build /p:Configuration=Release .\ALL_BUILD.vcxproj

Building Halide and LLVM on Windows using mingw

The makefile method above should work from inside a "mingw64" shell (not the default shell) in an msys2 installation.

If all else fails...

Do what the build-bots do:

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.

Some useful environment variables

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 size of the thread pool. This has no effect on OS X or iOS, where we just use grand central dispatch.

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.

Using Halide on OSX

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++.

For parallelism, Halide automatically uses Apple's Grand Central Dispatch, so it is not possible to control the number of threads used without overriding the parallel runtime entirely.

Halide OpenGL/GLSL backend

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.

Writing OpenGL-Based Filters

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


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);

JIT Compilation

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.

AOT Compilation

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 buffer_t::dev 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_int cannot 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 for Hexagon HVX

Halide supports offloading work to Qualcomm Hexagon DSP on Qualcomm Snapdragon 820 devices or newer. The Hexagon DSP provides a set of 64 and 128 byte vector instructions - the Hexagon Vector eXtensions (HVX). HVX is well suited to 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_64 or hexagon-32-qurt-hvx_128.

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_64 or hvx_128 target features 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 LLVM and Clang v5.0 or later from
  2. Download and install the Hexagon SDK and version 8.0 Hexagon Tools
  3. Build and run an example for Hexagon HVX

1. Obtain and build LLVM and clang v5.0 or later from

The Hexagon backend is currently under development. So it's best to use trunk llvm. These are the same instructions as above for building Clang/LLVM, but for trunk Clang/LLVM instead of 5.0.

cd <path to llvm>
svn co .
svn co ./tools/clang
# Or:
#    git clone .
#    git clone llvm/tools
mkdir build
cd build
make -j8
export LLVM_CONFIG=<path to llvm>/build/bin/llvm-config
export CLANG=<path to llvm>/build/bin/clang

2. Download and install the Hexagon SDK and version 8.0 Hexagon Tools

Go to

  1. Select the Hexagon Series 600 Software and download the 3.0 version for Linux.
  2. untar the installer
  3. Run the extracted installer to install the Hexagon SDK and Hexagon Tools, selecting Installation of Hexagon SDK into /location/of/SDK/Hexagon_SDK/3.0 and the Hexagon tools into /location/of/SDK/Hexagon_Tools/8.0
  4. Set an environment variable to point to the SDK installation location
export SDK_LOC=/location/of/SDK

3. Build and run an example for Hexagon HVX

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/v60/hexagon_sim_remote
export HL_HEXAGON_TOOLS=$SDK_LOC/Hexagon_Tools/8.0/Tools/
LD_LIBRARY_PATH=../../src/runtime/hexagon_remote/bin/host/:$HL_HEXAGON_TOOLS/lib/iss/:. HL_TARGET=host-hvx_128 make test

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

To build the example for Android, first ensure that you have a standalone toolchain created from the NDK using the script:

export ANDROID_NDK_HOME=$SDK_LOC/Hexagon_SDK/3.0/tools/android-ndk-r10d/
export ANDROID_ARM64_TOOLCHAIN=<path to put new arm64 toolchain>
$ANDROID_NDK_HOME/build/tools/ --arch=arm64 --platform=android-21 --install-dir=$ANDROID_ARM64_TOOLCHAIN

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

HL_TARGET=arm-64-android-hvx_128 ./
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