iotamudelta / hcc

HCC is an Open Source, Optimizing C++ Compiler for Heterogeneous Compute currently for the ROCm GPU Computing Platform

HCC : An open source C++ compiler for heterogeneous devices

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 C++ AMP, HC, C++ 17 ParallelSTL, or OpenMP, 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

Download HCC

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.

Build HCC from source

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

Use HCC

For C++AMP source codes:

hcc `clamp-config --cxxflags --ldflags` foo.cpp

For HC source codes:

hcc `hcc-config --cxxflags --ldflags` foo.cpp

In case you build HCC from source and want to use the compiled binaries directly in the build directory:

For C++AMP source codes:

# notice the --build flag
bin/hcc `bin/clamp-config --build --cxxflags --ldflags` foo.cpp

For HC source codes:

# notice the --build flag
bin/hcc `bin/hcc-config --build --cxxflags --ldflags` foo.cpp

Multiple ISA

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

use --amdgpu-target= command line option

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

# ISA for Hawaii(gfx701), Carrizo(gfx801), Tonga(gfx802) and Fiji(gfx803) would 
# be produced
hcc `hcc-config --cxxflags --ldflags` \
    --amdgpu-target=gfx701 \
    --amdgpu-target=gfx801 \
    --amdgpu-target=gfx802 \
    --amdgpu-target=gfx803 \
    foo.cpp

use HCC_AMDGPU_TARGET env var

Use , to delimit each AMDGPU target in HCC. Example:

export HCC_AMDGPU_TARGET=gfx701,gfx801,gfx802,gfx803
# ISA for Hawaii(gfx701), Carrizo(gfx801), Tonga(gfx802) and Fiji(gfx803) would 
# be produced
hcc `hcc-config --cxxflags --ldflags` foo.cpp

configure HCC use CMake HSA_AMDGPU_GPU_TARGET variable

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 Hawaii(gfx701), Carrizo(gfx801), Tonga(gfx802) and Fiji(gfx803) would 
# be produced by default
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DROCM_DEVICE_LIB_DIR=~hcc/ROCm-Device-Libs/build/dist/lib \
    -DHSA_AMDGPU_GPU_TARGET="gfx701;gfx801;gfx802;gfx803" \
    ../hcc

CodeXL Activity Logger

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 \
    -DHSA_AMDGPU_GPU_TARGET=<AMD GPU ISA version string> \
    -DROCM_DEVICE_LIB_DIR=<location of the ROCm-Device-Libs bitcode> \
    -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.

HCC with ThinLTO Linking

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

Set up your environment in the following way:

export KMTHINLTO=1

ThinLTO Phase 1 - Implemented

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.

ThinLTO Phase 2 - Under development

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.