Skip to content

HTTPS clone URL

Subversion checkout URL

You can clone with HTTPS or Subversion.

Download ZIP
a language for image processing and computational photography
C++ C Python LLVM Makefile CMake Other

Merge pull request #756 from kenkuang1213/master

Fix compile command of turtorial
latest commit f66f17f4d0
@abadams abadams authored

README.md

Halide is a programming language designed to make it easier to write high-performance image processing code on modern machines. Halide currently targets X86, ARM, CUDA, OpenCL, and OpenGL on OS X, Linux, and Windows.

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.

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=1 injects print statements into compiled Halide code that will describe what the program is doing at runtime. Higher values print more detail.

HL_TRACE_FILE=... specifies a binary target file to dump tracing data into. The output can be parsed programmatically by starting from the code in utils/HalideTrace.cpp

HL_PROFILE=1 injects timing data collection code. The output can be parsed using utils/HalideProf.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.

Building Halide

Building halide requires at least llvm 3.5, 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-3.5, 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_35 llvm3.5
% svn co https://llvm.org/svn/llvm-project/cfe/branches/release_35 llvm3.5/tools/clang

Then build it like so:

% cd llvm3.5
% ./configure --disable-terminfo --enable-optimized --enable-assertions --with-clang --enable-targets=x86,arm,nvptx
% make -j8

(Users of OSX 10.8+ may need to explicitly specify GCC vs Clang, prepending "CC=gcc CXX=g++" to the configure command.)

Then finally tell Halide's Makefile about it like so:

% export LLVM_CONFIG=<path to llvm>/Release+Asserts/bin/llvm-config
% export CLANG=<path to llvm>/Release+Asserts/bin/clang

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

% cd llvm3.5
% mkdir build
% cd build
% cmake -DLLVM_ENABLE_TERMINFO=OFF -DLLVM_TARGETS_TO_BUILD="X86;ARM;NVPTX" -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
export CLANG=<path to llvm>/build/bin/clang

With LLVM_CONFIG and CLANG set (or the appropriate llvm-config and clang in your path), you should be able to just run 'make' in this directory. '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 use cmake to build Halide, the build procedure is:

% mkdir build
% cd build
% LLVM_ROOT=/path/to/llvm3.5
% cmake -DLLVM_BIN=${LLVM_ROOT}/build/bin -DLLVM_INCLUDE="${LLVM_ROOT}/include;${LLVM_ROOT}/build/include" -DLLVM_LIB=${LLVM_ROOT}/build/lib -DLLVM_VERSION=35 ..
% make -j8

Building Halide and llvm as 32-bit on 64-bit linux

This is necessary if you want to JIT compile 32-bit code. It is not necessary for AOT compiling 32-bit Halide pipelines. The 64-bit version of Halide cross-compiles 32-bit code just fine.

To get a 32-bit llvm, configure and compile it like so:

% CC="gcc -m32" CXX="g++ -m32" ./configure --enable-targets=x86,arm,nvptx --enable-assertions --enable-optimized --build=i686-pc-linux-gnu
% CC="gcc -m32" CXX="g++ -m32" make

To generate a 32-bit Halide, compile it like so:

% HL_TARGET=x86-32 LD="ld -melf_i386" CC="gcc -m32" CXX="g++ -m32" make

You should then be able to run the JIT tests with a 32-bit target:

% CXX="g++ -m32 -msse4" make build_tests
% HL_TARGET=x86-32-sse41 make run_tests

If you have a 32-bit libpng, you can also run the apps in 32-bit:

% HL_TARGET=x86-32-sse41 CXX="g++ -m32 -msse4" make test_apps

The tests should pass, but the tutorials will fail to compile unless you manually supply a 32-bit libpng.

Building Halide with Native Client support

Halide is capable of generating Native Client (NaCl) object files and Portable Native Client (PNaCl) bitcode. JIT compilation is not supported. For both NaCl and PNaCl, the PNaCl llvm tree is used as it contains required llvm headers and libraries for compiling to all Native Client targets.

In order to build Halide with Native Client support, one will need the PNaCl llvm tree from:

https://chromium.googlesource.com/native_client/pnacl-llvm.git

and, for good measure, PNaCl's version of clang:

https://chromium.googlesource.com/native_client/pnacl-clang.git

To check these out:

% git clone https://chromium.googlesource.com/native_client/pnacl-llvm.git pnacl-llvm
% cd pnacl-llvm/tools
% git clone https://chromium.googlesource.com/native_client/pnacl-clang.git clang
% cd ../..

To enable all Halide targets, build it like so:

% mkdir build
% cd build
% cmake -DLLVM_TARGETS_TO_BUILD="X86;ARM;NVPTX" -DLLVM_ENABLE_ASSERTIONS=ON -DLLVM_ENABLE_TERMINFO=OFF -DCMAKE_BUILD_TYPE=Release ..
% make -j8

It will possibly be helpful to get the entire dev tree for PNaCl. Documentation for this is here:

http://www.chromium.org/nativeclient/pnacl/developing-pnacl

To use generated code in an application, you'll of course also need the Native Client SDK:

https://developer.chrome.com/native-client/sdk/download

Once The Native Client prerequisites are in place, set the following variables (on the command line or by editing the Makefile):

Point LLVM_CONFIG to the llvm-config that lives in your pnacl llvm build. E.g:

% export LLVM_CONFIG=<path-to-Halide>/llvm/pnacl-llvm/build/bin/llvm-config

Change WITH_NATIVE_CLIENT to "true" (or any non-empty value):

% export WITH_NATIVE_CLIENT=true

With these variables set, run make. This will build a Halide lib capable of generating native client objects. Neither the tests nor most of the apps Makefiles have been updated to work with cross compilation however. Try the app HelloNacl for a working example.

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

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);

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.

Limitations

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

  • Compute shaders

Something went wrong with that request. Please try again.