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 onemkl>
.Download and install the required dependencies manually.
Build Commands#
In most cases, building oneMKL Interfaces 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 onemkl>
mkdir build && cd build
cmake .. -DONEMKL_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 oneMKL 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