Calculations on GPU

GPU can be used for several types of calculations, e.g. of an undulator source. The gain is enormous. You should try it. Even on an “ordinary” Intel processor the execution with OpenCL becomes significantly faster.

Here are some benchmarks on a system with Intel Core i7-3930K 3.20 GHz CPU, ASUS Radeon R9 290 GPU, Python 2.7.6 64-bit, Windows 7 64-bit. Script examples\withRaycing\01_SynchrotronSources\synchrotronSources.py, 1 million rays, execution times in seconds:

CPU 1 process 5172, 1 CPU process loaded
CPU 10 processes 1245, with heavily loaded system
openCL with CPU 163, with highly loaded system
openCL with GPU 132, with almost idle CPU

You will need AMD/NVIDIA drivers (if you have a GPU, however this is not a must), a CPU only OpenCL runtime, pytools and pyopencl.

Note

When using OpenCL, no further parallelization is possible by means of multithreading or multiprocessing. You should turn them off by using the default values processes=1 and threads=1 in the run properties.

Please run the script tests\raycing\info_opencl.py for getting information about your OpenCL platforms and devices. You will pass then the proper indices in the lists of the platforms and devices as parameters to pyopencl methods or, alternatively, pass ‘auto’ to targetOpenCL.

Important

Consider the warnings and tips on using xrt with GPUs.

Hint

Consider also Speed tests for a few selected cases.