Sunday, January 9, 2011 - 2:30pm - 3:30pm
Cris Cecka (Stanford University)
see abstract for Lecture 1
Sunday, January 9, 2011 - 1:30pm - 2:30pm
Cris Cecka (Stanford University)
In this short course, we introduce the GPU as a coprocessor for scientific computing. The course will review modern hardware, CUDA programming, algorithm design, and optimization considerations for this unique compute environment. Introductory example codes and slides will be available to aid attendees in using GPUs to accelerate their applications.
Thursday, January 13, 2011 - 11:00am - 12:00pm
Mike Giles (University of Oxford)
Wednesday, January 12, 2011 - 8:30am - 9:30am
Cris Cecka (Stanford University)
We discuss multiple strategies to perform general computations on unstructured grids using a GPU, with specific application to the assembly of systems of equations in finite element methods (FEMs). For each method, we discuss the GPU hardware's limiting resources, optimizations, key data structures, and dependence of the performance with respect to problem size, element size, and GPU hardware generation. These methods are applied to a nonlinear hyperelastic material model to develop a large-scale real-time interactive elastodynamic visualization.
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