Introductory: 25% Intermediate: 50% Advanced: 25%
SC Conference - Activity Details
S04: High Performance Computing with CUDA
Presenters:| David P. Luebke (NVIDIA) |
| Ian A. Buck (NVIDIA) |
| Jon M. Cohen (NVIDIA) |
| John D. Owens (University of California, Davis) |
| Paulius Micikevicius (NVIDIA) |
| John E. Stone (University of Illinois at Urbana-Champaign) |
| Scott A. Morton (Hess Corporation) |
| Michael A. Clark (Boston University) |
| Sunday, 08:30AM - 05:00PM |
| Room Oregon Ballroom 204 |
NVIDIA's CUDA is a general purpose architecture for writing highly parallel applications. CUDA provides several key abstractions--a hierarchy of thread blocks, shared memory, and barrier synchronization--for scalable high-performance parallel computing. Scientists throughout industry and academia use CUDA to achieve dramatic speedups on production and research codes. The CUDA architecture supports many languages, programming environments, and libraries including C, Fortran, OpenCL, DirectX Compute, Python, Matlab, FFT, LAPACK implementations, etc.
In this tutorial NVIDIA engineers will partner with academic and industrial researchers to present CUDA and discuss its advanced use for science and engineering domains. The morning session will introduce CUDA programming, motivate its use with many brief examples from different HPC domains, and discuss tools and programming environments. The afternoon will discuss advanced issues such as optimization and sophisticated algorithms/data structures, closing with real-world case studies from domain scientists using CUDA for computational biophysics, fluid dynamics, seismic imaging, and theoretical physics.
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