This repository hosts the HCC compiler implementation project. The goal is to implement a compiler that takes a program that conforms to a parallel programming standard such as HC, C++ 17 ParallelSTL and transforms it into the AMD GCN ISA.

The project is based on LLVM+CLANG. For more information, please visit the hcc wiki:

https://github.com/RadeonOpenCompute/hcc/wiki

AMD is deprecating HCC to put more focus on HIP development and on other languages supporting heterogeneous compute. We will no longer develop any new feature in HCC and we will stop maintaining HCC after its final release, which is planned for June 2019. If your application was developed with the hc C++ API, we would encourage you to transition it to other languages supported by AMD, such as HIP or OpenCL. HIP and hc language share the same compiler technology, so many hc kernel language features (including inline assembly) are also available through the HIP compilation path.

The project now employs git submodules to manage external components it depends upon. It it advised to add --recursive when you clone the project so all submodules are fetched automatically.

For example:

# automatically fetches all submodules
git clone --recursive -b clang_tot_upgrade https://github.com/RadeonOpenCompute/hcc.git

For more information about git submodules, please refer to git documentation.

To configure and build HCC from source, use the following steps:

mkdir -p build; cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make

To install it, use the following steps:

sudo make install

For HC source codes:

hcc -hc foo.cpp -o foo

HCC now supports having multiple GCN ISAs in one executable file. You can do it in different ways:

It's possible to specify multiple --amdgpu-target= option. Example:

# ISA for Fiji(gfx803) and Vega10(gfx900) would 
# be produced
hcc -hc \
    --amdgpu-target=gfx803 \
    --amdgpu-target=gfx900 \
    foo.cpp

If you build HCC from source, it's possible to configure it to automatically produce multiple ISAs via HSA_AMDGPU_GPU_TARGET CMake variable.

Use ; to delimit each AMDGPU target. Example:

# ISA for Fiji(gfx803) and Vega10(gfx900) would 
# be produced by default
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DHSA_AMDGPU_GPU_TARGET="gfx803;gfx900" \
    ../hcc

To enable the CodeXL Activity Logger, use the USE_CODEXL_ACTIVITY_LOGGER environment variable.

Configure the build in the following way:

cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DUSE_CODEXL_ACTIVITY_LOGGER=1 \
    <ToT HCC checkout directory>

In your application compiled using hcc, include the CodeXL Activity Logger header:

#include <CXLActivityLogger.h>

For information about the usage of the Activity Logger for profiling, please refer to its documentation.

To enable the ThinLTO link time, use the KMTHINLTO environment variable.

Set up your environment in the following way:

export KMTHINLTO=1

For applications compiled using hcc, ThinLTO could significantly improve link-time performance. This implementation will maintain kernels in their .bc file format, create module-summaries for each, perform llvm-lto's cross-module function importing and then perform clamp-device (which uses opt and llc tools) on each of the kernel files. These files are linked with lld into one .hsaco per target specified.

This ThinLTO implementation which will use llvm-lto LLVM tool to replace clamp-device bash script. It adds an optllc option into ThinLTOGenerator, which will perform in-program opt and codegen in parallel.

Set up environmental variable:

export HCC_ENABLE_PRINTF=1

Then compile the printf kernel with HCC_ENABLE_ACCELERATOR_PRINTF macro defined.

~/build/bin/hcc -hc -DHCC_ENABLE_ACCELERATOR_PRINTF -lhc_am -o printf.out ~/hcc/tests/Unit/HSA/printf.cpp

For more examples on how to use printf, see tests in tests/Unit/HSA/printf*.cpp.