CUDA: Week in Review
Friday, January 29, 2010, Issue #6 - Newsletter Home  
Welcome to this week’s issue of "CUDA: Week in Review." Questions or suggestions? Email us at
New Computing Textbook Launched
An important new textbook was published this week: Programming Massively Parallel Processors: A Hands-on Approach. Co-written by Dr. David B. Kirk, NVIDIA Fellow, and Dr. Wen-mei Hwu of the University of Illinois at Urbana-Champaign, it teaches students how to program within a massively parallel environment and includes many case studies. The book utilizes CUDA C as well as OpenCL.

Here’s what people are saying:

David Patterson of U.C. Berkeley (and co-author of Computer Architecture: A Quantitative Approach): "For those interested in the GPU path to parallel enlightenment, this new book from David Kirk and Wen-mei Hwu is a godsend."
Hanspeter Pfister of Harvard: "David Kirk and Wen-mei Hwu are pioneers in this increasingly important field. This book will be the standard reference for years to come."
NVIDIA’s Chief Scientist Bill Dally: "I look forward to seeing the transformation of computing as students are inspired and guided to master GPU computing by this book."

If you are a professor, student, researcher, programmer, or simply a person who’s passionate about computing, this book will be of interest. For more info, go to Elsevier or Amazon.

Computing Simulation Tool Wins Best Paper Award
Virginia Tech associate professor Sandeep Shukla and three of his students earned a best paper award at the Asia and South Pacific Design Automation Conference (ASP-DAC). The paper explores the simulation performance of hardware models created in a language called SystemC, often used to shorten manufacturing design cycles to improve time to market. The team’s experiments, which utilized CUDA C, were carried out on an NVIDIA Tesla 870 and showed speed-ups of 30 to 100 times that of previous simulations. See:
Accelerating the Pace of Drug Discovery with GPUs
NVIDIA's Sumit Gupta blogs about how GPU computing is transforming drug discovery:

"Computer simulations of biochemical reactions help point scientists in the right direction and improve their productivity. The problem is, the computer simulations necessary for this type of research are so compute-intensive they’re typically done on supercomputers. It can take weeks or months on a supercomputer to simulate just one biochemical reaction.

For example, in order to simulate how tiny cellular mechanisms called ribosomes work to build proteins out of amino acids, it took scientists at Los Alamos National Laboratory more than eight months of supercomputer time to recreate a process that in reality takes about 2 nanoseconds. With about 15 million to 20 million researchers who could benefit from supercomputer access and roughly a couple thousand supercomputers, there’s just not enough computing resources to go around. Right now biochemists have to request time on these supercomputers a year in advance, and not all researchers even get access.

But if the right tools are available, scientists don’t have to wait to test their new ideas. This is where parallel computing can accelerate innovation, by removing the compute bottleneck represented by traditional supercomputers."

See full blog post:
New on CUDA Zone: GPU-Accelerated RNA Folding Algorithm
Extensive work has been done to predict the folding structures of RNA and DNA sequences. However, the high computational complexity of the algorithms, combined with the rapid increase of genomic data, has triggered the need for faster methods. In this research, CUDA C is used to harness the power of NVIDIA GPUs for general computation, achieving a 17x speed-up. Citation: Guillaume Rizk, Dominique Lavenier; Univ-Rennes 1/IRISA, ENS-Cachan/IRISA; Symbiose Campus Universitaire de Beaulieu, France. Lecture Notes in Computer Science, Springer Berlin / Heidelberg. See it here:
Submit Your Work
Have a CUDA-related paper or research? Show it on CUDA Zone. Submission form:
CUDA Education
CSC Offers CUDA Course in Finland
CSC, the Finnish IT Center for Science, is offering a course on CUDA and OpenCL for scientific computing on 2/1/10-2/2/10. See:
Call for Papers!
Dependable Multi-Core Computing Workshop. Papers due 2/15/10.
Graduate Fellowship Program
NVIDIA’s Graduate Fellowship Program applications are due 2/3/10. Learn more:
CUDA and GPU Computing Courses
Over 295 universities are teaching CUDA and GPU Computing courses. See the list:
Download the CUDA Toolkit 3.0 Beta here:
- Follow CUDA & GPU Computing on Twitter:
- Network with other developers at:
- Learn more about CUDA on CUDA Zone:
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