Tutorials

Tutorial 1 -- 13:00-15:00, March 4, 2008.

NVIDIA CUDA Software and GPU Parallel Computing Architecture

David B. Kirk
Chief Scientisit, NVIDIA

Abstract
David B. Kirk In the past, graphics processors were special purpose hardwired application accelerators, suitable only for conventional rasterization-style graphics applications. Modern GPUs are now fully programmable, massively parallel floating point processors. This talk will describe NVIDIA's massively multithreaded computing architecture and CUDA software for GPU computing. The architecture is a scalable, highly parallel architecture that delivers high throughput for data-intensive processing. Although not truly general-purpose processors, GPUs can now be used for a wide variety of compute-intensive applications beyond graphics.

This tutorial will introduce the Tesla architecture for massively parallel computing and describe the CUDA language programming environment. Basics of CUDA programming will be discussed, and application examples will be given.

Biography
David Kirk has been NVIDIA's Chief Scientist since January 1997. His contribution includes leading NVIDIA graphics technology development for today’s most popular consumer entertainment platforms. In 2007, Dr. Kirk was elected to the National Academy of Engineering (NAE) for his role in bringing high-performance graphics to personal computers. Election to the NAE is among the highest professional distinctions awarded in engineering. In 2002, Dr. Kirk received the SIGGRAPH Computer Graphics Achievement Award for his role in bringing high-performance computer graphics systems to the mass market. From 1993 to 1996, Dr. Kirk was Chief Scientist, Head of Technology for Crystal Dynamics, a video game manufacturing company. From 1989 to 1991, Dr. Kirk was an engineer for the Apollo Systems Division of Hewlett-Packard Company. Dr. Kirk is the inventor of 50 patents and patent applications relating to graphics design and has published more than 50 articles on graphics technology. Dr. Kirk holds B.S. and M.S. degrees in Mechanical Engineering from the Massachusetts Institute of Technology and M.S. and Ph.D. degrees in Computer Science from the California Institute of Technology.

Tutorial 2 -- 15:30-17:30, March 4, 2008.

Unsteady Flow Visualization

David Kao Han-Wei Shen
NASA Ames Research Center

Ohio State University

David Kao Han-Wei Shen
Abstract
This tutorial provides an in depth look at unsteady flow visualization algorithms for understanding the physics of complex flow fields generated from Computational Fluid Dynamics (CFD) simulations. The main focus is on two types of flow visualization methods: (1) off-body flow feature visualization and (2) surface flow visualization. These methods provide effective flow visualization that is effective in analyzing vortices and vortex interaction.

Off-body flow visualization identifies flow features surround the underlying grid surfaces and the most commonly used algorithms are based on particle tracking. This tutorial reviews algorithms for streamlines, streaklines, and timelines. A comparison of these different particle tracking based algorithms are shown. Often, instantaneously streamlines are used for identify off-body flow features in unsteady flows. This tutorial compares streamlines with streaklines (and timelines), which are time-dependent particle tracking algorithms. The tutorial also reviews current feature extraction algorithms that can be used to extract vortex cores.

Surface flow visualization identifies flow features near the grid surfaces. In particle-based tracking algorithms, seeding is a common challenging since the overall flow feature revealed depends on where the particles are seeded (e.g., released). Recently, several streamline placement algorithms have been developed for a wide variety of applications. This tutorial reviews these algorithms and compares their applicability to CFD applications.

A wide variety of synthetic texture-based algorithms have been developed to depict near-body flow features. The most common approach is based on the Line Integral Convolution (LIC) algorithm. Recently there are also extensions of LIC to support more flexible texture generations for 3D flow data. This tutorial reviews these algorithms. Most existing algorithms are applicable to the flow at an instance in time. This tutorial compares these steady surface flow methods with unsteady algorithms.

David Kao's Biography
David is a researcher in the NASA Advanced Supercomputing (NAS) Division at Ames Research Center. He has developed numerous collaborations (both internal and external to NASA) and created applications for scientific visualization in various disciplines. David has led several innovative software projects at NASA Ames. One of the software codes is named UFAT (Unsteady Flow Analysis Toolkit), which effectively reduces the analysis time of multi-gigabyte datasets from weeks to hours. He received a NASA Space Act Award 2003 for UFAT and its significant contributions to the aeronautics user community. David served as an Associate Editor for IEEE Transactions on Visualization and Computer Graphics (2003-2007). He was a subtopic manager for Small Business Innovation Research in data management and visualization (2002-2003) and a research advisor for the National Research Council (since 1998). He has also taught computer graphics courses at Santa Clara University and was an Associate Adjunct Profession at the University of California, Santa Cruz (2005-2007). David's fields of interest include computer graphics, scientific visualization, information visualization, and numerical flow visualization.

Han-Wei Shen's Biography
Han-Wei Shen is an Associate Professor at The Ohio State University. He received his BS degree from Department of Computer Science and Information Engineering at National Taiwan University in 1988, the MS degree in computer science from the State University of New York at Stony Brook in 1992, and the PhD degree in computer science from the University of Utah in 1998. From 1996 to 1999, he was a research scientist at NASA Ames Research Center in Mountain View California. His primary research interests are scientific visualization and computer graphics. Professor Shen is a winner of National Science Foundation's CAREER award and US Department of Energy's Early Career Principal Investigator Award. He also won an Outstanding Teaching award in the Department of Computer Science and Engineering at the Ohio State University.