[talks] Friday Feb 22 noon: Ian Buck->John Danskin, nVidia, "GPU Computing"

Adam Finkelstein af at CS.Princeton.EDU
Fri Feb 22 09:49:05 EST 2008

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

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 <af at cs.princeton.edu> 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.

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