Dear list members, Just to be clear up front, this is an offer of collaboration in research. This is not a job offer. There is no pay offered. I expect that the research would result in one or more academic papers published in journals. That would be the only personal benefit of the collaboration.
I'm working on some statistical estimation methods based on matching data to simulated nonparametric moments that are fitted using kernel regression. This can be useful when moments are not calculable analytically. The kernel regression part is computationally demanding, especially when the data is high dimensional. Doing kernel smoothing basically requires calculating the matrix of distances between the N points x (each point has K coordinates) and P points y (also of dimension K). So the problem is to fill out the NxP matrix D, where D_ij is the distance between x_i and y_j. This is pretty obviously easy to parallelize, and I have done this using MPI. I'm interested in trying this with CUDA though pycuda. However, I'm not very handy with pycuda, and not even all that handy with Python, and doing this myself would be pretty slow. So, if anyone with Python and pycuda skills is interested in collaborating on some research that I'm confident would lead to one or more published academic papers, I'd be interested in discussing it with you. If interested, please contact me directly at michael.creel AT uab.es. Please let's not clutter up this forum. Thanks to Andreas for permission to post this message. Michael Creel _______________________________________________ PyCuda mailing list [email protected] http://tiker.net/mailman/listinfo/pycuda_tiker.net
