Appendix F: expert-level options
This section catalogs very advanced functionality that is stable and well-tested, but switched off by default.
CUDA GPU support
Several functions in Spinach can make use of CUDA GPUs. If your computer has a recent NVidia graphics card, enabling that functionality may be beneficial. This is done by adding 'gpu' to the enable array:
sys.enable={'gpu'};
Spinach kernel modules that can make use of GPU arithmetic are:
- Matrix exponential calculation in propagator.m function.
- Matrix inverse-times-vector operation during the slow-passage detection in slowpass.m function.
- Krylov propagation in krylov.m and step.m functions.
Numerical pseudocontact shift solvers also support GPUs for the Fourier solver option. GPU support is enabled in ipcs.m and kpcs.m by specifying
options.gpu=1;
For the typical 128x128x128 point grids used in paramagnetic centre probability density reconstructions from PCS, using a Tesla K40 card results in up to an order of magnitude acceleration relative to 32 CPU cores.
Note that commodity NVidia graphics cards (e.g. GeForce) have artificially capped 64-bit floating-point performance - the Tesla range is more expensive, but much recommended.
Propagator caching
Spinach may be instructed to keep a disk record of the matrices that propagator.m function has previously seen, so that propagators are not recomputed, but instead fetched from the disk next time the matrix is encountered. This can save large amounts of time in simulations of very repetitive pulse sequences. To turn this functionality on, add 'caching' to the enable array:
sys.enable={'caching'};
The cached propagators are placed into /scratch directory in the Spinach root folder.
