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CUDA Week in Review Newsletter
Thurs., Dec. 12, 2013, Issue #104 Newsletter Home
Welcome to CUDA: Week In Review, news for the worldwide CUDA, GPGPU and parallel programming community.
CUDA Pro Tip: Vectorized memory accesses are a fundamental CUDA optimization that can be used to increase bandwidth utilization while reducing instruction count latency. Learn more at Parallel Forall.
GPU Tech Conference Update: GTC 2014 registration is now open. Special code for newsletter readers: GM20CD. Call for Posters is open through Jan. 31.


Dr. Debbie BardCUDA-Accelerated Cosmology
This week’s Spotlight is on Dr. Debbie Bard of the Kavli Institute for Particle Astrophysics and Cosmology. KIPAC is an independent laboratory of Stanford University. Its mission is to bring the resources of modern computational, experimental, observational and theoretical science to bear on our understanding of the universe at large. Read the Spotlight.


SC13 Wrap UpSC13 Wrap Up
SC13 in Denver attracted 10,600+ attendees and 335 exhibitors. Highlights included the Student Cluster competition, won by a team from the University of Texas at Austin using NVIDIA GPUs.

NVIDIA’s SC13 news announcements included: Student-Led CUDA Lab at Virginia Tech
Kudos to the ACM Student Chapter at Virginia Tech for hosting a CUDA tutorial and lab. Organizer Carlo del Mundo led students through hands-on exercises using the cloud-based qwikLABS platform, which makes several thousand GPU-powered Amazon EC2 servers available for online training. Students learned to write and launch CUDA C/C++ kernels, manage GPU memory, and manage communication and synchronization. If you are a student or teacher and would like to offer a GPU programming lab at your university, contact

Compute the Cure Grant Winner
More than 50 universities and research organizations from around the world recently submitted proposals for a $200,000 grant from the NVIDIA Foundation. The winner is Dr. Rommie Amaro, University of California, San Diego. Dr. Amaro is working on a shareable GPU-accelerated workflow to help speed the development of drugs to fight cancer. This grant is part of the Foundation’s Compute the Cure initiative.

CUDA + Adobe
The newest release of Adobe Creative Cloud adds even more GPU-accelerated solutions for creative video pros including debayering in Premiere Pro CC for ultra-high definition 4K formats, a new Direct Link Color Pipeline in SpeedGrade CC, and GPU-enhanced Adobe Media Encoder for faster video and image processing.

Test Drive Tesla K40 Today
With cutting-edge features like GPUBoost and 12GB of memory, Tesla K40 delivers up to 40% more performance than Tesla K20X. You can try Tesla K40 for free by registering for a GPU Test Drive.

Upcoming Webinars
Dec. 12: Running OpenACC Programs, M. Wolfe, PGI
Dec. 12: GPU-Accelerated Geospatial Line-of-Sight Calculations, F. Suykens, B. Adams, Luciad
Dec. 17: NVIDIA GRID VCA: High-Performance Remoting Solution for SolidWorks, A. Patel, NVIDIA
Jan. 16: Folding@home and OpenMM: Simulating Protein Dynamics, V. Pande, Stanford University
Jan. 21: Using HOOMD-blue for Polymer Simulations and Big Systems, J.Glaser, Univ. of Michigan


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Subscribe to the Parallel Forall RSS feed Parallel Forall:
CUDACasts Episode #12: Programming GPUs using CUDA Python, by Mark Ebersole
CUDA Pro Tip: Increase Performance with Vectorized Memory Access, by Justin Luitjens
Subscribe to NVIDIA RSS feed NVIDIA:
12-Year-Old Learns to Program Graphics Chips, by Brian Caulfield
GPU-Powered System Wins Student Supercomputing Competition, by Mark Ebersole
More Than Skin Deep: P&G Using GPUs to Study Skincare Products, by George Millington
How One Researcher Used 18,000 GPUs..., by Brian Caulfield


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NVIDIA seeks senior software engineer to work on CUDA system software team. Ideal candidate will have very strong C programming skills, thorough understanding of operating systems and kernel programming, working knowledge of CUDA and parallel programming, a good understanding of hardware architecture, and excellent communication and planning skills. See, #1635268.


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Want to improve your technical skills? Sign up for Intro to Parallel Programming.
Need CUDA advice? See list of worldwide CUDA trainers and consultants.
Have CUDA questions? Check out NVIDIA DevTalk forums and Stack Overflow.
Require fast access to docs? Visit the CUDA doc library.


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4-Day CUDA Course – Finance Focus (Acceleware)
  Dec. 10-13, 2013, New York, New York

UK Many-Core Developer Conference (University of Oxford)
  Dec. 16, 2013, Oxford, England
Note: Includes CUDA "masterclass" sessions

Parallel and Distributed Computing in Geoscience and Remote Sensing
  Dec. 15-18, 2013, Seoul, Korea
Note: IEEE Workshop


CUDA Programming Course (Delft University of Technology)
  Jan. 24, 2014, Delft, Netherlands
Teachers: C. Vuik and Ir. C.W.J. Lemmens

  March 1-5, 2014, Salt Lake City, Utah

GPU Technology Conference (GTC 2014)
  March 24-27, 2014, San Jose, Calif.

IEEE Int’l Parallel & Distributed Processing Symposium
  May 19-23, 2014, Phoenix, Arizona

(To list an event, email:


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Online CUDA Course in Russian

Try out this new course designed for Russian speakers.


Check out our new series of short videos about CUDA.

Tell Us Your CUDA Story

If you are a CUDA developer, tell us how you are using CUDA. Your submission will be considered for display on the NVIDIA website.

GPU-Accelerated Apps

See updated list of 240+ GPU-accelerated applications.

GPU Meetups

Learn about Meetups in your city, or start one up.

The Best of GTC 2013, Online

GTC 2013 featured over 400 sessions on breakthroughs made with GPUs in science, technology, and industry. Experience it by visiting

GPU Test Drive

Want to try Tesla K20 for free? Sign up here.

CUDA Documentation

The new CUDA documentation site includes release notes, programming guides, manuals and code samples.

Online Learning

Udacity | Coursera

NVIDIA Developer Forums

Join us on the NVIDIA DevTalk forums to share your experience and learn from other developers. You can also ask questions on Stack Overflow, using the ’cuda’ tag.

CUDA Consulting

Training, programming and project development services are available from CUDA consultants around the world. To be considered for inclusion on list, email:

GPU Computing on Twitter

For daily updates about GPU computing and parallel programming, follow @gpucomputing on Twitter.



CUDA on the Web

CUDA Spotlights
CUDA Newsletters
GPU Test Drive

NVIDIA Newsletters

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CUDA® is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU). NVIDIA provides a complete toolkit for programming on the CUDA architecture, supporting standard computing languages such as C, C++ and Fortran. Send comments to
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