[Aspuru-Guzik group list] Fwd: TALK: Easy, Effective, Efficient: GPU Programming in Python with PyOpenCL and PyCUDA (Andreas Kloeckner, Thursday March 31st, 2011, 7:35 PM, Harvard MD G125)

Hello: For those in the group interested in python programming. Roberto Begin forwarded message: *From: *Nicolas Pinto <pinto(a)mit.edu> *Date: *March 30, 2011 9:10:55 AM EDT *To: *undisclosed-recipients:; *Subject: **TALK: Easy, Effective, Efficient: GPU Programming in Python with PyOpenCL and PyCUDA (Andreas Kloeckner, Thursday March 31st, 2011, 7:35 PM, Harvard MD G125)* Title: Easy, Effective, Efficient: GPU Programming in Python with PyOpenCL and PyCUDA Speaker: Andreas Kloeckner (NYU) Date: 3-31-2011 Time: 7:35 PM Location: Harvard Hall 104 **Room change** Harvard CS264 2011 Guest Lecture Series "Massively Parallel Computing" Course (http://www.cs264.org) Host: Nicolas Pinto (Harvard, MIT) Abstract: High-level scripting languages are in many ways polar opposites to GPUs. GPUs are highly parallel, subject to hardware subtleties, and designed for maximum throughput, and they offer a tremendous advance in the performance achievable for a significant number of computational problems. On the other hand, scripting languages such as Python favor ease of use over computational speed and do not generally emphasize parallelism. PyOpenCL and PyCUDA are two packages that attempt to join the two together. By showing concrete examples, both at the toy and the whole-application level, this talk aims to demonstrate that by combining these opposites, a programming environment is created that is greater than just the sum of its two parts. Speaker biography: Andreas Klöckner obtained his PhD degree working with Jan Hesthaven at the Department of Applied Mathematics at Brown University. He worked on a variety of topics all aiming to broaden the utility of discontinuous Galerkin (DG) methods. This included their use in the simulation of plasma physics and the demonstration of their particular suitability for computation on throughput-oriented graphics processors (GPUs). He also worked on multi-rate time stepping methods and shock capturing schemes for DG. In the fall of 2010, he joined the Courant Institute of Mathematical Sciences at New York University as a Courant Instructor. There, he is working on problems in computational electromagnetics with Leslie Greengard. His research interests include: - Discontinuous Galerkin and integral equation methods for wave propagation - Programming tools for parallel architectures - High-order unstructured particle-in-cell methods for plasma simulation -- Nicolas Pinto, PhD http://web.mit.edu/pinto