Skip to content
/ hcc Public
forked from ROCm/hcc

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

License

Notifications You must be signed in to change notification settings

AlexVlx/hcc

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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 Activiy Logger header:

#include <CXLActivityLogger.h>

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

About

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

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • C++ 98.0%
  • CMake 0.7%
  • Perl 0.6%
  • Shell 0.4%
  • C 0.2%
  • Python 0.1%