The talk is as planned at noon; however, due to a canceled flight we have a change of speaker and slight change in emphasis: Speaker: John Danskin Title: NVIDIA GeForce 8800 Graphics Processor: development and direction Time/Place: Today, noon, Computer Science Bldg, room 302 Abstract: The GeForce 8800 and its derivative architectures are the fastest available graphics processors. In an architectural departure, they are significantly more flexible than previouos processors. In this talk Dr. Danskin will discuss our goals for the 8800, the decision making process, new architectural features, performance, and some challenges for the future. Dr. Danskin is Vice President of GPU Architecture at NVIDIA, Chief Architect for the GeForce 8800 GPU, and holds a PhD in Computer Science from Princeton University. On Wed, Feb 20, 2008 at 10:58 PM, Adam Finkelstein wrote:

Talk announcement:

Speaker: Ian Buck, nVidia Title: GPU Computing Time/Place: Friday, noon, Computer Science Bldg, room 302

Abstract:

Many researchers have observed that general purpose computing with programmable graphics hardware (GPUs) has shown promise to solve many of the world's compute intensive problems, many orders of magnitude faster the conventional CPUs. The challenge has been working within the constraints of a graphics programming environment to leverage this huge performance potential. GPU computing with CUDA is a new approach to computing where hundreds of on-chip processor cores simultaneously communicate and cooperate to solve complex computing problems, transforming the GPU into a massively parallel processor. The NVIDIA C-compiler for the GPU provides a complete development environment gives developers the tools they need to solve new problems in computation-intensive applications such as product design, data analysis, technical computing, and game physics. In this talk, I will provide a brief history of computing with GPUs, how CUDA can solve compute intensive problems, and where GPU computing will be going in the future.

Bio:

Ian Buck completed his Ph.D. at the Stanford Graphics Lab in 2004. His thesis was titled "Stream Computing on Graphics Hardware," researching programming models and computing strategies for using graphics hardware as a general purpose computing platform. His work included developing the "Brook" software toolchain for abstracting the GPU as a general purpose streaming coprocessor. Ian received is B.S.E from Princeton and was a proud member of the Princeton CS Graphics Group. He currently works for NVIDIA as the GPU-Compute software manager.