At SC11, NVIDIA® (booth# 2719) will be showcasing the advances in applications and research with GPU computing and scientific discovery. We invite you to visit our booth to learn more about how parallel computing is driving the industry trend in heterogeneous computing.
New computers using parallel processor such as Tesla™ GPUs, companion processors to the CPU, are accelerating HPC applications by 10x. Stop by the NVIDIA booth and find out how.
Conference Keynote by Jen-Hsun Huang, founder and CEO of NVIDIA
Tuesday, November 15, 8:30AM
Kicking off this year's conference is the keynote by Jen-Hsun Huang, founder and CEO of NVIDIA. An on-demand version of the keynote will be available soon, please check back here.
GPU Technology Theater @ SC11
Monday, November 15 – Thursday, November 17 during exhibition hours | NVIDIA Booth #2719
Click on the Theater tab for more details, or Download the PDF schedule.
To view or download presentations from the GPU Technology Theater at SC11 please visit GTC On Demand.
CUDA Research Fast Forward
Monday, November 14th, 20:00PM|NVIDIA Booth
Presented by the Pasi Fellows, Moderated by Lorena Barba, Boston University
Albert Sidelnik, University of Illinois Urbana-Champaign
Christopher Cooper, Boston University
Trevor Gokey, San Francisco State University
Olesiy Karpenko, University of Illinois Chicago
Anush Krishnan, Boston University
Simon Layton, Boston University
Ying-Wai Li, University of Georgia
Britton Olson, Stanford University,
Juan Perilla, University of Illinois Urbana-Champaign
Benjamin Payne, Missouri University of Science and Technology
For a detailed listing of the CUDA Research being presented, please Download the PDF here.
Tutorial: High Performance Computing with CUDA
Monday, November 14, 8:30AM-5:00PM
*See below to download PDF copies of the presentations.
This tutorial will introduce CUDA to the supercomputing audience and motivate its use with traditional HPC examples. We will first teach the basics of CUDA programming with step-by-step walkthroughs of code samples, then review the main optimizations techniques, and describe profiling and tuning best practices to maximize performance. While CUDA C and CUDA Fortran will be used for illustration, the concepts covered will apply equally to programs written with the OpenCL and DirectCompute APIs. Finally, we will close with case studies from academia and industry.
Download Introduction PDF
Download CUDA C Basics PDF
Download CUDA Fortran and Libraries PDF
Download Performance Optimization PDF
Download Multi-GPU Programming PDF
Download Unrolling Parallel Loops PDF
Download CUDA Accelerated Monte Carlo for HPC PDF