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 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.
| Linux |
|---|
 |
Have llvm-8.0 (or greater) installed and run make in the root
directory of the repository (where this README is).
Building Halide requires at least LLVM 8.0, along with the matching
version of Clang; we recommend using the most recent stable version of LLVM for
most users (LLVM 9.0 at the time of this writing). llvm-config and clang
must be somewhere in the path. If your OS does not have packages for llvm-8.0
(or newer), 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 subversion.
% svn co https://llvm.org/svn/llvm-project/llvm/branches/release_90 llvm9.0
% svn co https://llvm.org/svn/llvm-project/cfe/branches/release_90 llvm9.0/tools/clang
Then build it like so:
% cd llvm9.0
% mkdir build
% cd build
% cmake -DLLVM_ENABLE_TERMINFO=OFF -DLLVM_TARGETS_TO_BUILD="X86;ARM;NVPTX;AArch64;Mips;PowerPC" -DLLVM_ENABLE_ASSERTIONS=ON -DCMAKE_BUILD_TYPE=Release ..
% make -j8
then to point Halide to it:
export LLVM_CONFIG=<path to llvm>/build/bin/llvm-config
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
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.
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
% cmake -DCMAKE_INSTALL_PREFIX=../llvm-install -DLLVM_ENABLE_TERMINFO=OFF -DLLVM_TARGETS_TO_BUILD=X86;ARM;NVPTX;AArch64;Mips;Hexagon -DLLVM_ENABLE_ASSERTIONS=ON -DLLVM_BUILD_32_BITS=OFF -DCMAKE_BUILD_TYPE=Release ../llvm -G "Visual Studio 14 Win64"
For a 32-bit build use:
% cmake -DCMAKE_INSTALL_PREFIX=../llvm-install -DLLVM_ENABLE_TERMINFO=OFF -DLLVM_TARGETS_TO_BUILD=X86;ARM;NVPTX;AArch64;Mips;Hexagon -DLLVM_ENABLE_ASSERTIONS=ON -DLLVM_BUILD_32_BITS=ON -DCMAKE_BUILD_TYPE=Release ../llvm -G "Visual Studio 14"
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
The makefile method above should work from inside a "mingw64" shell (not the default shell) in an msys2 installation.
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 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_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 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,
- Obtain and build LLVM and Clang v5.0 or later from llvm.org
- Download and install the Hexagon SDK and version 8.0 Hexagon Tools
- Build and run an example for Hexagon HVX
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 http://llvm.org/svn/llvm-project/llvm/trunk .
svn co http://llvm.org/svn/llvm-project/cfe/trunk ./tools/clang
# Or:
# git clone http://llvm.org/git/llvm.git .
# git clone http://llvm.org/git/clang.git llvm/tools
mkdir build
cd build
cmake -DLLVM_ENABLE_TERMINFO=OFF -DLLVM_TARGETS_TO_BUILD="X86;ARM;NVPTX;AArch64;Mips;PowerPC;Hexagon" -DLLVM_ENABLE_ASSERTIONS=ON -DCMAKE_BUILD_TYPE=Release ..
make -j8
export LLVM_CONFIG=<path to llvm>/build/bin/llvm-config
Go to https://developer.qualcomm.com/software/hexagon-dsp-sdk/tools
- Select the Hexagon Series 600 Software and download the 3.0 version 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/3.0and the Hexagon tools into/location/of/SDK/Hexagon_Tools/8.0 - 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/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 the example for Android, first ensure that you have a standalone toolchain created from the NDK using the make-standalone-toolchain.sh 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>
$ANDROID_NDK_HOME/build/tools/make-standalone-toolchain.sh --arch=arm64 --platform=android-21 --install-dir=$ANDROID_ARM64_TOOLCHAIN
Now build and run the blur example using the script to run it on device:
export HL_HEXAGON_TOOLS=$SDK_LOC/HEXAGON_Tools/8.0/Tools/
HL_TARGET=arm-64-android-hvx_128 ./adb_run_on_device.sh