cutde 21.4.5.2

130 million TDEs per second, Python + CUDA TDEs from Nikkhoo and Walter 2015

cutde

Python + CUDA TDEs from Nikkhoo and Walter 2015

CUDA and OpenCL-enabled fullspace triangle dislocation elements. Benchmarked at 130 million TDEs per second. Based on the original MATLAB code from Nikhoo and Walter 2015..

See below for usage and installation instructions.

Basics

Howdy! Usage is really simple:

import cutde

disp = cutde.disp(pts, tris, slips, 0.25)
strain = cutde.strain(pts, tris, slips, nu)

• pts is a np.array with shape (N, 3)
• tris is a np.array with shape (N, 3, 3) where the second dimension corresponds to each vertex and the third dimension corresponds to the cooordinates of those vertices.
• slips is a np.array with shape (N, 3) where slips[:,0] is the strike slip component, while component 1 is the dip slip and component 2 is the tensile/opening component.
• the last parameter, nu, is the Poisson ratio.

IMPORTANT: N should be the same for all these arrays. There is exactly one triangle and slip value used for each observation point.

• The output disp is a (N, 3) array with displacement components in the x, y, z directions.
• The output strain is a (N, 6) array representing a symmetric tensor. strain[:,0] is the xx component of strain, 1 is yy, 2 is zz, 3 is xy, 4 is xz, and 5 is yz.

I want stress.

Use:

stress = cutde.strain_to_stress(strain, sm, nu)

to convert from stress to strain assuming isotropic linear elasticity. sm is the shear modulus and nu is the Poisson ratio.

All pairs

If, instead, you want to create a matrix representing the interaction between every observation point and every source triangle, there is a different interface:

import cutde

disp = cutde.disp_all_pairs(pts, tris, slips, 0.25)
strain = cutde.strain_all_pairs(pts, tris, slips, nu)

• pts is a np.array with shape (N_OBS_PTS, 3)
• tris is a np.array with shape (N_SRC_TRIS, 3, 3) where the second dimension corresponds to each vertex and the third dimension corresponds to the cooordinates of those vertices.
• slips is a np.array with shape (N_SRC_TRIS, 3) where slips[:,0] is the strike slip component, while component 1 is the dip slip and component 2 is the tensile/opening component.
• the last parameter, nu, is the Poisson ratio.
• The output disp is a (N_OBS_PTS, N_SRC_TRIS, 3) array.
• The output strain is a (N_OBS_PTS, N_SRC_TRIS, 6) array.

Note that to use the strain_to_stress function, you'll need to reshape the output strain to be (N_OBS_PTS * N_SRC_TRIS, 6).

Installation

Just run

pip install cutde

Then, if you have an NVIDIA GPU, install PyCUDA with:

conda install -c conda-forge pycuda

If not, you'll need to install PyOpenCL. Installing OpenCL is sometimes a breeze and sometimes a huge pain, but it should be installable on most recent hardware and typical operating systems. These directions can be helpful.. I am happy to try to help if you have OpenCL installation issues, but I can't promise to be useful. For me, on an Ubuntu + Intel machine, I just ran:

conda install -c conda-forge pyopencl ocl-icd-system

and everything worked wonderfully.

Development

For developing cutde, clone the repo and set up your conda environment based on the environment.yml with:

conda env create

Next, install either pycuda or pyopencl as instructed in the Installation section above.

Then, you need to generate the baseline test data derived from the MATLAB code from Mehdi Nikhoo. To do this, first install octave. On Ubuntu, this is just:

sudo apt-get install octave

And run

./tests/setup_test_env

which will run the tests/matlab/gen_test_data.m script.

Finally, to check that cutde is working properly, run pytest!

The library is extremely simple:

• cutde.fullspace - the main entrypoint.
• fullspace.cu - a direct translation of the original MATLAB into CUDA/OpenCL. This probably should not be modified.
• cutde.gpu - a layer that abstracts between CUDA and OpenCL
• cutde.cuda - the PyCUDA interface.
• cutde.opencl - the PyOpenCL interface.