Building the Project with AdaptiveCpp#

Environment Setup#

Build and install AdaptiveCpp. For a detailed description of available AdaptiveCpp backends, their dependencies, and installation, see the AdaptiveCpp installation readme.
Clone this project. The root directory of the cloned repository will be referred to as <path to onemath>.
Download and install the required dependencies manually.

Build Commands#

In most cases, building oneMath is as simple as setting the compiler and selecting the desired backends to build with.

On Linux (other OSes are not supported with the AdaptiveCpp compiler):

# Inside <path to onemath>
mkdir build && cd build
cmake .. -DONEMATH_SYCL_IMPLEMENTATION=hipsycl   \ # Indicate that AdaptiveCpp is being used.
        -DENABLE_MKLGPU_BACKEND=False            \ # MKLGPU backend is not supported by AdaptiveCpp
        -DENABLE_<BACKEND_NAME>_BACKEND=True     \ # Enable backend(s) (optional)
        -DENABLE_<BACKEND_NAME_2>_BACKEND=True   \ # Multiple backends can be enabled at once.
        -DHIPSYCL_TARGETS=omp/;hip:gfx90a,gfx906 \ # Set target architectures depending on supported devices.
        -DBUILD_FUNCTIONAL_TESTS=False           \ # See section *Building the tests* for more on building tests. True by default.
        -DBUILD_EXAMPLES=False                   # Optional: True by default.
cmake --build .
cmake --install . --prefix <path_to_install_dir> # required to have full package structure

Backends should be enabled by setting -DENABLE_<BACKEND_NAME>_BACKEND=True for each desired backend. By default, the MKLGPU and MKLCPU backends are enabled, but MKLGPU must be disabled with AdaptiveCpp. The supported backends for the compilers are given in the table at oneMath supported configurations table, and the CMake option names are given in the table below. Some backends may require additional parameters to be set. See the relevant section below for additional guidance. The target architectures must be specified with HIP_TARGETS. See the AdaptiveCpp documentation.

If a backend library supports multiple domains (i.e. BLAS, RNG), it may be desirable to only enable selected domains. For this, the TARGET_DOMAINS variable should be set. For further details, see _build_target_domains.

By default, the library also additionally builds examples and tests. These can be disabled by setting the parameters BUILD_FUNCTIONAL_TESTS and BUILD_EXAMPLES to False. Building the functional tests may require additional external libraries. See the section Building and Running Tests for more information.

The most important supported build options are:

CMake Option	Supported Values	Default Value
ENABLE_MKLCPU_BACKEND	True, False	True
ENABLE_CUBLAS_BACKEND	True, False	False
ENABLE_CURAND_BACKEND	True, False	False
ENABLE_NETLIB_BACKEND	True, False	False
ENABLE_ROCBLAS_BACKEND	True, False	False
ENABLE_ROCRAND_BACKEND	True, False	False
ENABLE_MKLCPU_THREAD_TBB	True, False	True
BUILD_FUNCTIONAL_TESTS	True, False	True
BUILD_EXAMPLES	True, False	True
TARGET_DOMAINS (list)	blas, rng	All supported domains

Some additional build options are given in Additional Build Options.

Backends#

Building for CUDA#

The CUDA backends can be enabled with ENABLE_CUBLAS_BACKEND and ENABLE_CURAND_BACKEND.

The target architecture must be set using the HIPSYCL_TARGETS parameter. For example, to target a Nvidia A100 (Ampere architecture), set -DHIPSYCL_TARGETS=cuda:sm_80, where the figure 80 corresponds to a CUDA compute capability of 8.0. The correspondence between compute capabilities and Nvidia GPU products is given on the Nvidia website. Multiple architectures can be enabled using a comma separated list. See the AdaptiveCpp documentation.

No additional parameters are required for using CUDA libraries. In most cases, the CUDA libraries should be found automatically by CMake.

Building for ROCm#

The ROCm backends can be enabled with ENABLE_ROCBLAS_BACKEND and ENABLE_ROCRAND_BACKEND.

The target architecture must be set using the HIPSYCL_TARGETS parameter. See the AdaptiveCpp documentation. For example, to target the MI200 series, set -DHIPSYCL_TARGETS=hip:gfx90a. Multiple architectures can be enabled using a comma separated list. For example, -DHIPSYCL_TARGETS=hip:gfx906,gfx90a, and multiple APIs with a semicolon (-DHIPSYCL_TARGETS=omp\;hip:gfx906,gfx90a).

For common AMD GPU architectures, see the Building for ROCm in the DPC++ build guide.

Project Cleanup#

Most use-cases involve building the project without the need to clean up the build directory. However, if you wish to clean up the build directory, you can delete the build folder and create a new one. If you wish to clean up the build files but retain the build configuration, following commands will help you do so.

# If you use "GNU/Unix Makefiles" for building,
make clean

# If you use "Ninja" for building
ninja -t clean

Building the Project with AdaptiveCpp

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