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HCC supports heterogeneous offload to AMD APUs and discrete GPUs via HSA enabled runtimes and drivers. It is an ISO compliant C++ 11/14 compiler. It is based on Clang, the LLVM Compiler Infrastructure and the “libc++” C++ standard library.
Inspired by C++ AMP and C++14, this is the default C++ compute API for the HCC compiler. HC has some important differences from C++ AMP including removing the “restrict” keyword, supporting additional data types in kernels, providing more control over synchronization and data movement, and providing pointer-based memory allocation. It is designed to expose cutting edge compute capabilities on Boltzmann and HSA devices to developers while offering the productivity and usability of C++.
HIP provides a set of tools and API for converting CUDA applications into a portable C++ API. An application using the HIP API could be compiled by hcc to target AMD GPUs. Please refer to HIP's repository for more information.
Microsoft C++ AMP is a C++ accelerator API with support for GPU offload. This mode is compatible with Version 1.2 of the C++ AMP specification.
HCC provides an initial implementation of the parallel algorithms described in the ISO C++ Extensions for Parallelism, which enables parallel acceleration for certain STL algorithms.
HCC supports OpenMP 3.1 on CPU. The support for OpenMP 4.x accelerator offloading is currently in development.
Accelerated applications could be run on Radeon discrete GPUs from the Fiji family (AMD R9 Nano, R9 Fury, R9 Fury X, FirePro S9300 x2, Polaris 10, Polaris 11) paired with an Intel Haswell CPU or newer. HCC would work with AMD HSA APUs (Kaveri, Carrizo); however, they are not our main support platform and some of the more advanced compute capabilities may not be available on the APUs.
HCC currently only works on Linux and with the open source ROCK kernel driver and the ROCR runtime (see Installation for details). It will not work with the closed source AMD graphics driver.
This backend compiles GPU kernels into native GCN ISA, which could be directly execute on the GPU hardware. It's being actively developed by the Radeon Technology Group in LLVM.
Before continuing with the installation, please make sure any previously installed hcc compiler has been removed from on your system.
Install ROCm and make sure it works correctly.
Support for 14.04 has been deprecated.
Follow the instruction here to setup the ROCm apt repository and install the rocm-dkms or the rocm-dev meta-package.
Follow the instruction here to setup the ROCm apt repository and install the rocm-dkms or the rocm-dev meta-package.
Follow the instruction here to setup the ROCm yum rpm repository and install the rocm-dkms meta-package for CentOS/RHEL 7 Support.
Please follow steps to prepare devtoolset-7 environment which is needed for compiling HCC from source. This environment only requires to be installed once, but must enter the environment before compiling using command:
scl enable devtoolset-7 bash
Note: CentOS7 cmake is outdated, will need to use alternate cmake3.
Currently, HCC support for openSUSE is experimental and the compiler has to be built from source.
First, install the build dependencies:
# Ubuntu 14.04
sudo apt-get install git cmake make g++ g++-multilib gcc-multilib libc++-dev \
libc++1 libc++abi-dev libc++abi1 python findutils libelf1 libpci3 file \
debianutils libunwind8-dev hsa-rocr-dev hsa-ext-rocr-dev hsakmt-roct-dev \
pkg-config rocm-utils
# Ubuntu 16.04
sudo apt-get install git cmake make g++ g++-multilib gcc-multilib python \
findutils libelf1 libpci3 file debianutils libunwind-dev hsa-rocr-dev \
hsa-ext-rocr-dev hsakmt-roct-dev pkg-config rocm-utils
# Fedora 24
sudo dnf install git cmake make gcc-c++ python findutils elfutils-libelf \
pciutils-libs file pth rpm-build libunwind-devel hsa-rocr-dev hsa-ext-rocr-dev \
hsakmt-roct-dev pkgconfig rocm-utils
# CentOS 7
sudo yum install git cmake3 make gcc-c++ python findutils elfutils-libelf \
pciutils-libs file pth rpm-build redhat-lsb-core pkgconfig \
hsa-rocr-dev hsa-ext-rocr-dev hsakmt-roct-dev rocm-utils
# openSUSE Leap 42.3
sudo zypper install git cmake make gcc-c++ python python-xml findutils elfutils \
pciutils-devel file rpm-build libunwind-devel pkg-config libpth-devel
# install libc++ from OSB
sudo zypper addrepo \
-f http://download.opensuse.org/repositories/devel:/tools:/compiler/openSUSE_Leap_42.3/ devel_tools_compiler
sudo zypper update
sudo zypper install libc++-devel
Clone the HCC source tree:
# automatically fetches all submodules
git clone --recursive -b clang_tot_upgrade \
https://github.com/RadeonOpenCompute/hcc.git
Create a build directory and run cmake to configure the build:
mkdir build; cd build
cmake ../hcc
Compile HCC:
make -j
Run the unit tests:
make test
Create an installer package (DEB or RPM file)
make package
Here's a simple saxpy example written with the hc API.
To compile and link in a single step:
# Assume HCC is installed and added to PATH
# Notice the the hcc-config command is between two backticks
hcc `hcc-config --cxxflags --ldflags` saxpy.cpp -o saxpy
To build with separate compile and link steps:
# Assume HCC is installed and added to PATH
# Notice the the hcc-config command is between two backticks
hcc `hcc-config --cxxflags` saxpy.cpp -c -o saxpy.cpp.o
hcc `hcc-config --ldflags` saxpy.cpp.o -o saxpy
By default, HCC would auto-detect all the GPUs available it's running on and set the correct GPU architectures.
Users could use the --amdgpu-target=<GCN Version> option to compile for a specific architecture and to disable the auto-detection. The following table shows the different versions currently supported by HCC.
There exists an environment variable HCC_AMDGPU_TARGET to override the default GPU architecture globally for HCC; however, the usage of this environment variable is NOT recommended as it is unsupported and it will be deprecated in a future release.
| GCN Version | GPU/APU Family | Examples of Radeon GPU |
|---|---|---|
| gfx701 | GFX7 | FirePro W8100, FirePro W9100, Radeon R9 290, Radeon R9 390 |
| gfx801 | Carrizo APU | FX-8800P |
| gfx803 | GFX8 | R9 Fury, R9 Fury X, R9 Nano, FirePro S9300 x2, Radeon RX 480, Radeon RX 470, Radeon RX 460 |
| gfx900 | GFX9 | Vega10 |