CUDA: Week in Review
Tue., September 20, 2011, Issue #62 - Newsletter Home
Welcome to CUDA: Week in Review, an online news summary for the worldwide CUDA, GPU computing and parallel programming ecosystem.
Mehdi Raessi, UMass-Dartmouth
Test Drive Tesla MD SimCluster
New CUDA Centers Announced
MATLAB Acceleration on GPUs
HP Mini-Supercomputer
Running CUDA on x86
CUDA Jobs: European Commission
Sign up to be a CUDA Registered Developer
Follow @GPUComputing on Twitter
CUDA Spotlight
GPU-Accelerated Multi-Phase Flow Simulations
This week’s spotlight is on Dr. Mehdi Raessi, Assistant Professor in the Department of Mechanical Engineering at University of Massachusetts-Dartmouth.
NVIDIA: Mehdi, tell us about your research.
Dr. Mehdi RaessiMehdi: The focus of my research is primarily on multi-phase flows and free-surface flows with phase change. We develop computational algorithms and flow solvers, and use them to study industrial and research applications that involve multi-phase flows.

Examples include materials processing (thermal spray coating and casting), energy systems (both renewable and conventional), and environmentally friendly or "green" refrigeration systems.
NVIDIA: What role does GPU computing play in your work?
Mehdi: Our numerical algorithm for solving the fluid flow equations involves a step in which we solve a large system of linear equations to compute the pressure field. That single step can take from 50 to 99.9 percent of the total simulation time! As we increase the number of grid points in our simulations, the pressure solution step takes a larger percentage of the total simulation time.

To speed up this task, my graduate student, Stephen Codyer, ported the pressure calculations to the GPU. His tests show that the GPU-accelerated solver can run a 3D simulation with over 28 million grid points 15 times faster (compared to performing the same calculation on the CPU). My colleague, Prof. Gaurav Khanna, from our Physics Department, helped us a lot in this project and shared his extensive experience in GPU computing.
NVIDIA: What future applications can you envision in your research area?
Mehdi: As we all know, energy and the environment have become the most pressing issues in the world. Addressing these issues requires new technology and drastic changes in the ways that we use our energy resources. After events like the oil spill in the Gulf of Mexico and the Fukushima Daiichi nuclear disaster, I think everyone agrees that we should plan to use energy resources that have low potential to cause catastrophic events.

We have begun projects that are targeting these issues. With GPU-accelerated computational tools, we are now able to study much larger problems at a level of detail that was not feasible before. These simulations can lead to new energy devices that are more efficient and have less environmental impact. I believe the capability to run faster and faster simulations with GPUs will one day enable us to predict, respond to and mitigate catastrophic events.
  - Read the complete interview here

  (Would you like to be in the CUDA Spotlight? Email
CUDA Developer News
Test Drive the Tesla MD SimCluster back to the top
Want to test drive a GPU? Try the new Tesla Molecular Dynamics SimCluster, which is preconfigured to accelerate AMBER or NAMD. All you need to do to start a simulation is load your models. To test drive, visit or email mdsimcluster (at)
New CUDA Centers Announced
Congrats to all the new CUDA Research Centers and CUDA Teaching Centers, including Carnegie Mellon/Silicon Valley and the University of Edinburgh. For the complete list of newcomers, see this recent blog post:

MATLAB Acceleration on GPUs
Using MATLAB for GPU computing is ideal for engineers and scientists who want to take advantage of GPUs without low-level C or Fortran programming. Register for a free webinar on October 27 to learn how CUDA-enabled GPUs can help accelerate MATLAB computations:
For more info about GPU computing in MATLAB, see this recent MATLAB Digest article:

Mini-Supercomputer from HP
The new GPU Starter Kit from HP provides a ready-to-use GPU computing cluster, straight out of the box. It combines eight HP ProLiant SL390 G7 servers (based on 24 Tesla M2070 GPUs) with 16 CPUs. For info, visit or email Hpc-sales (at)

Running CUDA on x86
In Dr. Dobb’s newsletter, Rob Farber writes: "Recent developments allow CUDA programs to transparently compile and run at full speed on x86 architectures. This advance makes CUDA a viable programming model for all application development, just like OpenMP. The PGI CUDA C/C++ compiler for x86 (from the Portland Group Inc.) is the reason for this recent change in mindset."
- Learn more:
Replays of the Week
New CUDA Research and Teaching Centers, by Chandra Cheij
Dive into Windows 8 with NVIDIA, by Stephen Jones
NVIDIA GPUDirect for Video and More, by Greg Estes
back to the top
CUDA Zone Blog
GPU Starter Kit Stirs Excitement, by Nadeem Mohammad
Replay of the Week
NEW: Each week we highlight a session from GTC 2010. Here is our pick for this week: back to the top
      Using GPUs for Real-Time Brain-Computer Interfaces (GTC 10)
      Adam Wilson – University of Cincinnati
NEW: The Joint Research Centre of the European Commission is seeking a CUDA programmer with image processing experience to support operational emergency mapping initiatives. The position is based in Italy. Application deadline is Oct. 3, 2011.
- See:
back to the top
Feel free to attend these upcoming GPU Meetups! The atmosphere is casual and collaborative. Participants and sponsors are warmly welcomed.

- United States
        New York GPU Meetup – Sept. 26, 6:00 pm
        South Florida GPU Meetup – Sept. 26, 6:30 pm
        Boston GPU Meetup – Oct. 6, 6:00 pm
        Silicon Valley GPU Meetup – Oct. 10, 6:15 pm
        New York GPU Meetup – Oct. 24, 6:00 pm (Special joint meeting with
        C++ Dev Group)
SC11: NVIDIA GTC Express Live Theater (Nov. 12-18)
Poster deadline: Sept. 27

GTC Asia 2011 (Dec. 14-15)
Poster deadline: Oct. 25

GTC U.S. 2012 (May 14-17)
Session deadline: Nov. 3
Poster deadline: Dec. 8

Mathematics: SIAM 12 (July 9-13)
Paper and award deadline: Oct. 1

Bioinformatics: BICoB 2012 (March 12-14)
Paper deadline: Oct. 28
CUDA Calendar
September 2011 back to the top
- SEG (Society of Exploration Geophysicists) Annual Meeting
Sept. 18-23, 2011, San Antonio, Texas.

- CUDA 3-Day Training Course — Tech-X
Sept. 19-21, 2011, Boulder, Colorado
Presented by Tech-X

- Russian-German HPC School
Sept. 19-30, 2011, Novosibirsk

- SPIE Conference on High-Performance Computing in Remote Sensing
Sept. 19-22, 2011, Prague, Czech Republic

- CUDA Optimization: (Webinar)
Sept. 20, 2011
Presented by Timothy Schroeder, NVIDIA

- CUDA Optimization: (Webinar)
Sept. 27, 2011
Presented by Gernot Ziegler, NVIDIA

- GPU-Accelerated Derivative Pricing Models — SciComp
Sept. 27, 2011, New York
Presented by SciComp, NVIDIA, Dell, Microsoft

- NEW: CUDA 4-Day Training Course — Acceleware
Sept. 27-30, 2011, London, UK
Presented by Acceleware with Microsoft

October 2011

- CUDA Optimization (Webinar)
Oct. 4, 2011
Presented by Paulius Micikevicius, NVIDIA

- NEW: CUDA 4-Day Training Course - Acceleware
Oct. 11-14, 2011, Los Angeles, Calif.
Presented by Acceleware with Microsoft

- GPU-Accelerated Derivative Pricing Models — SciComp
Oct. 17, 2011, London, UK
Presented by SciComp, NVIDIA, Dell, Microsoft

- NEW: GPU Computing with MATLAB (Webinar)
Oct. 27, 2011
Presented by Sarah Wait Zaranek, MathWorks

November 2011

- NEW: CUDA 4-Day Training Course — Acceleware
Nov. 17-4, 2011, Frankfurt, Germany
Presented by Acceleware with Microsoft

- Supercomputing 2011 (SC11)
Nov. 12-18, Seattle, Washington

- NEW: GPU Programming for Defense/Intelligence — AccelerEyes (Webinar)
Nov. 15, 2011
Learn to accelerate common defense and intelligence algorithms using easy, powerful programming libraries, with Jacket for use with MATLAB and LibJacket for C/C++/Fortran.

- NEW: CUDA Training (Basic and Advanced) — CAPS
Nov. 22-24, 2011, Rennes, France
Email: training (at)

December 2011

- NEW: AGU (American Geophysical Union) Meeting
Dec. 5, 2011, San Francisco
Session on High-Res Modeling Using GPU and Many-Core Architectures

- NEW: GTC Asia
Dec. 14-15, 2011, Beijing, China
Featuring the latest GPU computing breakthroughs, demos and presentations.

- NEW: LibJacket CUDA Library for Maximus — AccelerEyes (Webinar)
Dec. 15, 2011
Learn to integrate computations with visualizations in a CUDA-based app through simple visualization functions for plotting, image and volume rendering, and more.

(To list an event, email:

CUDA Resources
Downloads back to the top
– CUDA 4.0:
– Parallel Nsight:
– Parallel Nsight:
CUDA Registered Developer Program
– Sign up:
– List of CUDA-enabled GPUs:
CUDA on the Web
– See previous issues of CUDA: Week in Review:
– Follow CUDA & GPU Computing on Twitter:
– Network with other developers:
– Stayed tuned to GPGPU news and events:
– Learn more about CUDA on CUDA Zone:
– Check out the NVIDIA Research page:
CUDA Recommended Reading
– Future of Computing Performance:
– Supercomputing for the Masses, Part 21:
– CUDA books:
CUDA Recommended Viewing
– The Third Pillar of Science:
– GTC 2010 presentations:
– SC10 presentations:
About CUDA
CUDA is NVIDIA’s parallel computing hardware architecture. NVIDIA provides a complete toolkit for programming on the CUDA architecture, supporting standard computing languages such as C, C++ and Fortran as well as APIs such as OpenCL and DirectCompute. Send comments and suggestions to:
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