Thank you for you answer. If I understand correctly, the highlighted GeForce GTX 770M in the GPUBench report is the speed-up from my own GPU and the main host is my CPU against the CPU used for the pre-stored data?
Im still not clear on what the results are telling me. Perhaps the report could include a bit more explanation?
Im finding that my computer (host pc) is considerably slower than the exact same card (Nvidia GTX 770M) in the pre-stored data. Are there any recommendations that may improve this? ny recommended reading?
Thank you very much for your submission. In your example the magnitude of the gradient and trigradient seem to differ. Presumably, it is due to vector scaling. Could you elaborate on this? Accurate scaling is essential of course.
Not only a great tool, the source code and the discussions here are a great education. Thanks, John, for extending the docs and examples
It usually does everything I need. When really off-the-wall needs have come up, it's been a great base to start from. Clear and well structured code.
15 Dec 2014
Plot georeferenced vector fields with color options.
Hi John! Happy to see you are still cranking and contributing your genius to the MATLAB community. Are you still experimenting with Python anymore? FYI: Python has a great symbolic toolbox called SymPy (http://www.sympy.org/en/index.html) similar to MATLAB and Maple, completely free, and available with the Anaconda Scientific Python Distribution from Continuum Analytics (https://store.continuum.io/cshop/anaconda/). Also you should seriously get on GitHub (https://github.com/). Take Care!
Of course, the simple answer for Chad is to put a basic wrapper around gridfit, one that tests the data, and compares it to the nodes in advance. If the nodes were not chosen to contain the data, then fix them so they are. No warning need ever be generated then. Then just have the wrapper code pass all arguments into gridfit.