CUDA Spotlight: GPU-Accelerated Large-Scale Analytics
This week's spotlight is on Dr. Ren Wu, Senior Research Scientist at HP Labs in Palo Alto, Calif., and Principal Investigator of the CUDA Research Center at HP Labs. Dr. Wu works in the area of large-scale analytics and business intelligence.
NVIDIA: Ren, tell us about what you are working on.
NVIDIA: What are some potential applications of GPU-accelerated large-scale data analytics?
NVIDIA: How does GPU computing play a role in your work?
NVIDIA: What kind of advantages have you achieved with CUDA?
Personally, I think that the CUDA programming model is a very nice framework – well balanced on abstraction and expressing power, enough control for algorithm designers, and supported by hardware with exceptional performance (compared to other alternatives).
In particular, I like the fact that the algorithm designer can manually manage cache (shared memory to global memory, and global memory to PC memory). For me, one of the key requirements for high performance computing on a many-core architecture is the ability to optimize against the memory hierarchy.
NVIDIA: How did you become interested in this area?
Of course, the release of CUDA changed everything. When I first saw CUDA, I knew instantly that this could be a game changer. That was when we started to look at GPU computing seriously.
NVIDIA: As computing becomes faster, what will we be able to do in the future that we are not able to do today?
Dr. Ren Wu’s bio
Dr. Ren Wu is a Senior Research Scientist at HP Labs, Palo Alto. His research interests include data-intensive high-performance computing, massively parallel algorithms and computational intelligence. In recent years he has been focusing on GPU acceleration of large-scale analytics, and is well known for his work on GPU-accelerated clustering algorithms. He is also the PI of the CUDA Research Center at HP Labs.