GPGPU

The most computationally intensive parts of gprMax, which are the FDTD solver loops, can optionally be executed using General-purpose computing on graphics processing units (GPGPU). This has been achieved through use of the NVIDIA CUDA programming environment, therefore a NVIDIA CUDA-Enabled GPU is required to take advantage of the GPU-based solver.

Extra installation steps for GPU usage

The following steps provide guidance on how to install the extra components to allow gprMax to run on your NVIDIA GPU:

  1. Install the NVIDIA CUDA Toolkit. You can follow the Installation Guides in the NVIDIA CUDA Toolkit Documentation You must ensure the version of CUDA you install is compatible with the compiler you are using. This information can usually be found in a table in the CUDA Installation Guide under System Requirements.
  2. You may need to add the location of the CUDA compiler (nvcc) to your user path environment variable, e.g. for Windows C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin or Linux/macOS /Developer/NVIDIA/CUDA-10.0/bin.
  3. Install the pycuda Python module. Open a Terminal (Linux/macOS) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment conda activate gprMax. Run pip install pycuda

Running gprMax using GPU(s)

Open a Terminal (Linux/macOS) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment conda activate gprMax

Run one of the test models:

(gprMax)$ python -m gprMax user_models/cylinder_Ascan_2D.in -gpu

Note

If you want to select a specific GPU card on your system, you can specify an integer after the -gpu flag. The integer should be the NVIDIA CUDA device ID for a specific GPU card. If it is not specified it defaults to device ID 0.

Combining MPI and GPU usage

Message Passing Interface (MPI) has been utilised to implement a simple task farm that can be used to distribute a series of models as independent tasks. This is described in more detail in the OpenMP, MPI, HPC section. MPI can be combined with the GPU functionality to allow a series models to be distributed to multiple GPUs on the same machine (node). For example, to run a B-scan that contains 60 A-scans (traces) on a system with 4 GPUs:

(gprMax)$ python -m gprMax user_models/cylinder_Bscan_2D.in -n 60 -mpi 5 -gpu

Note

The argument given with -mpi is number of MPI tasks, i.e. master + workers, for MPI task farm. So in this case, 1 master (CPU) and 4 workers (GPU cards).