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INSTALL.md

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Building COSMA

To build COSMA, do the following steps:

# clone the repository
git clone --recursive https://github.com/eth-cscs/COSMA.git
cd COSMA

# create a build directory within COSMA
mkdir build
cd build

# set up the compiler, e.g. with:
export CC=`which cc`
export CXX=`which CC`

# Choose which BLAS and SCALAPACK backends to use (e.g. MKL)
cmake -DCOSMA_BLAS=MKL -DCOSMA_SCALAPACK=MKL ..

# compile
make -j 8

!! Note the --recursive flag !!

Other important options that can be passed to cmake are the following:

  • COSMA_BLAS: MKL (default), OPENBLAS, CRAY_LIBSCI, CUSTOM, CUDA or ROCM. Determines which backend will be used for the local matrix multiplication calls.
  • COSMA_SCALAPACK: OFF (default), MKL, CRAY_LIBSCI, CUSTOM. If specified, COSMA will also provide ScaLAPACK wrappers, thus offering pdgemm, psgemm, pzgemm and pcgemm functions, which completely match the ScaLAPACK API.

Building COSMA on Multi-GPU Systems

COSMA can take advantage of fast GPU-to-GPU interconnects like NV-Links, through the use of the following:

  • NCCL library (for NVIDIA GPUs), i.e. RCCL library (for AMD GPUs): when -DCOSMA_WITH_NCCL=ON, i.e. -DCOSMA_WITH_RCCL=ON is specified in cmake, all the collective communication is performed through these libraries, which can utilize fast gpu-to-gpu interconnects.
  • GPU-aware MPI: when -DCOSMA_WITH_GPU_AWARE_MPI=ON is specified in cmake, cuda-aware MPI for NVIDIA GPUs (i.e. rocm-aware MPI for AMD GPUs) will be used for collective communication. The user must make sure that the gpu-aware MPI is enabled. For example, on Cray-systems, this can be done by setting the following environment variables:
    • export MPICH_RDMA_ENABLED_CUDA=1
    • export MPICH_GPU_SUPPORT_ENABLED=1

Building COSMA on Cray Systems

There are already prepared scripts for loading the necessary dependencies for COSMA on Cray-Systems:

  • Cray XC40 (CPU-only version): source ./scripts/piz_daint_cpu.sh loads MKL and other neccessary modules.
  • Cray XC50 (Hybrid version): source ./scripts/piz_daint_gpu.sh loads cublas and other necessary modules.

After the right modules are loaded, the instructions from the beginning of this file can be followed.

Installing COSMA

To install do make install.

!! Note: To set custom installation directory use CMAKE_INSTALL_PREFIX when building.

COSMA is CMake friendly and provides a cosmaConfig.cmake module for easy integration into 3rd-party CMake projects with

find_package(cosma REQUIRED)
target_link_libraries( ... cosma::cosma)

COSMA's dependencies are taken care of internally, nothing else needs to be linked. Make sure to set CMAKE_INSTALL_PREFIX to COSMA's installation directory when building.

There is a rudimentary pkgconfig support; dependencies are handles explicitly by consumers.

Installing COSMA with Spack

  • with OpenBLAS back end: spack install cosma
  • with MKL back end: spack install cosma ^mkl
  • with GPU back end: spack install cosma +cuda
  • with Netlib LAPACK: spack install cosma ^netlib-lapack
  • with MKL ScaLAPACK: spack install cosma +scalapack ^mkl

Notes:

  • By default Spack builds in release mode with debug information included (-O2 -g). To build with -O3, add build_type=Release to the command line.
  • By default Spack selects openmpi as the MPI implementation, to select MPICH, add ^mpich

For more information on Spack: Spack 101 Tutorial.

Docker

COSMA can be installed into a Docker container in the following way:

docker build -f docker/gpu/build-env.Dockerfile -t cosma-build-env .
docker build --build-arg BUILD_ENV=cosma-build-env -f docker/gpu/deploy.Dockerfile -t cosma .

Then the cosma container can be deployed for testing:

docker run --rm -it -v (pwd):(pwd) --gpus all cosma