GPU Computing

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. Looking forward, the shift to heterogeneous based computing is exciting new territory for languages, operating systems, compilers and architectures and will share some of NVIDIA's vision of that 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 former member of the Princeton CS Graphics Group. He is currently Software Director for GPU Computing.