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AdePT

Accelerated demonstrator of electromagnetic Particle Transport

Build Requirements

The following packages are a required to build and run:

  • CMake >= 3.18
  • C/C++ Compiler with C++14 support
  • CUDA Toolkit (tested 10.1, min version TBD)
  • VecCore library 0.7.0 (recommended, but older versions >= 0.5.0 also work)
  • VecGeom library >= 1.1.20
  • G4HepEM library

A suitable environment may be set up either from CVMFS (requires the sft.cern.ch and projects.cern.ch repos to be available on the local system):

$ source /cvmfs/sft.cern.ch/lcg/views/devAdePT/latest/x86_64-centos7-gcc11-opt/setup.sh

or from the supplied spack environment file:

$ spack env create adept-spack ./scripts/spack.yaml
$ spack -e adept-spack concretize -f
$ spack -e adept-spack install
...
$ spack env activate -p adept-spack

Note that the above assumes your spack configuration defaults to use a suitable C++ compiler and has cuda_arch set appropriately for the hardware you will be running on.

You can also build the packages manually as follows. To configure and build VecCore, simply run:

$ cmake -S. -B./veccore-build -DCMAKE_INSTALL_PREFIX="<path_to_veccore_installation>"
$ cmake --build ./veccore-build --target install

Add your CUDA installation to the PATH and LD_LIBRARY_PATH environment variables, as in:

$ export PATH=${PATH}:/usr/local/cuda/bin
$ export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64

Find the CUDA architecture for the target GPU. If you installed the CUDA demo suite, the fastest way is to use the deviceQuery executable from extras/demo_suite. This lists the CUDA capability for all installed GPUs, remember the value for your target:

$ /usr/local/cuda/extras/demo_suite/deviceQuery
Device 0: "GeForce RTX 2080 SUPER"
  CUDA Capability Major/Minor version number:    7.5 (cuda_architecture=75)
...
Device 1: "Quadro K4200"
  CUDA Capability Major/Minor version number:    3.0 (cuda_architecture=30)

To configure and build VecGeom, use the configuration options below, using as <cuda_architecture> the value from the step above:

$ cmake -S. -B./vecgeom-build \
  -DCMAKE_INSTALL_PREFIX="<path_to_vecgeom_installation>" \
  -DCMAKE_PREFIX_PATH="<path_to_veccore_installation>" \
  -DVECGEOM_ENABLE_CUDA=ON \
  -DVECGEOM_GDML=ON \
  -DBACKEND=Scalar \
  -DCMAKE_CUDA_ARCHITECTURES=<cuda_architecture> \
  -DVECGEOM_USE_NAVINDEX=ON \
  -DCMAKE_BUILD_TYPE=Release
$ cmake --build ./vecgeom-build --target install -- -j6 ### build using 6 threads and install

To configure and build G4HepEM, use the configuration options below:

$ cmake -S. -B./g4hepem-build \
  -DCMAKE_INSTALL_PREFIX="<path_to_g4hepem_installation>" \
  -DCMAKE_PREFIX_PATH="<path_to_geant4_installation>" \
  -DG4HepEm_CUDA_BUILD=ON
$ cmake --build ./g4hepem-build --target install -- -j6 ### build using 6 threads and install

To configure AdePT, simply run:

$ cmake -S. -B./adept-build <otherargs>

As one needs to provide the paths to the dependence libraries VecCore, VecGeom and G4HepEM

   -DCMAKE_PREFIX_PATH="<path_to_veccore_installation>;<path_to_vecgeom_installation>;<path_to_g4hepem_installation>" \
   -DCMAKE_CUDA_ARCHITECTURES=<cuda_architecture> \
   -DCMAKE_BUILD_TYPE=Release

To build, run:

$ cmake --build ./adept-build -- -j6 ### build using 6 threads

The provided examples and tests can be run from the build directory:

$ cd adept-build
$ CUDA_VISIBLE_DEVICES=0 BuildProducts/bin/<executable>   ### use the device number matching the selected <cuda_architecture>

Including AdePT in other CMake projects

In order to include AdePT in a separate project we need to run:

find_package(AdePT)

Which has the same dependencies as before (VecGeom, VecCore and G4HepEM).

Then, for the targets using AdePT: