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 GPU:

  1. Install the NVIDIA CUDA Toolkit. You can follow the Installation Guides in the NVIDIA CUDA Toolkit Documentation
  2. 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 source activate gprMax (Linux/macOS) or activate gprMax (Windows). 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 source activate gprMax (Linux/macOS) or activate gprMax (Windows)

Run one of the test models:

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

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).