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CUDA Week in Review Newsletter
Tues., Jan. 22, 2013, Issue #87 Newsletter Home
Welcome to CUDA: WEEK IN REVIEW, a news summary for the worldwide CUDA, GPGPU and parallel programming community.
Top 10 Recommendations!
To usher in 2013, the NVIDIA team compiled a list of recommended activities for GPU computing fans:

1. Sign up today for GTC 2013 in San Jose, Calif.
2. Enroll in free Udacity Parallel Programming course
3. Download CUDA 5
4. Register as a CUDA Developer
5. Tell us how you are using CUDA
6. Test drive the new Kepler GPU accelerator
7. Join or start a GPU Meetup in your city
8. Nominate a colleague as a CUDA Spotlight
9. Watch presentation on CUDA by Stephen Jones
10. Learn about OpenACC

And, please take a minute to fill out our reader survey so we can improve the newsletter in 2013. Thanks!

CUDA SPOTLIGHT
Bob Zigon GPU-Accelerated Life Sciences
This week’s Spotlight is on Bob Zigon of Beckman Coulter. Bob’s focus is on GPU acceleration of Beckman Coulter’s life sciences products.

Bob was a GTC presenter in 2010 and 2012. The topic of his upcoming GTC 2013 talk is Computing Protein Size Distributions Using Centrifugation Techniques and the Tesla K20 GPU. Read our interview with Bob Zigon.

CUDA EDUCATION
Parallel Programming Course to Launch in February
Udacity’s Intro to Parallel Programming course begins in just a few weeks. This brand-new online class consists of seven units (lectures/homework/quizzes) which you can take at your own pace. You’ll learn about parallel programming by coding a series of image processing algorithms, such as you might find in Photoshop or Instagram.

People who successfully complete the course will earn a Udacity certificate, a badge of accomplishment that can be posted on LinkedIn, job websites and resumes. Intro to Parallel Programming is open to students and professionals around the world at no charge.

In addition, we are pleased to announce that Amazon Web Services will provide on-demand access to GPU compute instances on Amazon EC2 to qualifying students. While EC2 is not required to complete the course, it’s a helpful option for students who want to experiment with parallel and distributed computing on CUDA GPUs.

The course is taught by Dr. David Luebke of NVIDIA and Dr. John Owens of UC Davis. Guest lecturers include Ian Buck, Bill Dally and Stephen Jones of NVIDIA.

   • Watch the video about Intro to Parallel Programming
   • Sign up for Intro to Parallel Programming
   • Learn more about Udacity

Learn more about Amazon EC2

CUDA NEWS
Try Tesla K20
Speed up your application with NVIDIA Tesla K20 GPU Accelerators. Built on the Kepler compute architecture, Tesla K20 offers innovative technologies like Dynamic Parallelism and Hyper-Q to boost performance and power efficiency.
Special offer: Purchase a Tesla K20 GPU Accelerator by Jan. 27 and receive a free GTC 2013 pass (US only).
GPU test drive: Take a free and easy test drive to see how Tesla K20 can accelerate your code.

CUDA JOB OF THE WEEK

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GIS Federal seeks a Geospatial/GPU Software Engineer to develop high performance computation engine for geospatial intelligence. You will work with latest technologies including GPU supercomputing, Google Earth and 3D gaming engines. Requires TS/SCI security clearance. Learn more here.

FROM THE BLOGOSPHERE

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Subscribe to the Parallel Forall RSS feed New on the Parallel Forall Blog:
Join Me at GTC 2013, by Mark Harris
Using Shared Memory in CUDA Fortran, by Greg Ruetsch
CUDA Pro Tip: Flush Denormals with Confidence, by Mark Harris
How to Access Global Memory Efficiently in CUDA C/C++ Kernels, by Mark Harris
How to Access Global Memory Efficiently in CUDA Fortran Kernels, by Greg Ruetsch

(Subscribe to the Parallel Forall RSS feed)

GPU MEETUPS

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Find a GPU Meetup in your location, or start one up. Upcoming meetings include:
Brisbane, Jan. 24
New York, Jan. 24
Silicon Valley, Jan. 28
Minnesota, Jan. 29
Paris, Feb. 18

CUDA CALENDAR

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4-Day CUDA Course (Acceleware)
  Jan. 29-Feb. 1, 2013, Chicago, Illinois
Instructor: Dr. Kelly Goss

Titan Users and Developers Workshop (West Coast)
  Jan. 29-31, 2013, Santa Clara, Calif.
Hands-on training on Titan, the world’s fastest supercomputer

Signal Processing & Communications Algorithms Using GPU Computing (Webinar)
  Jan. 31, 2013
Instructor: Kirthi Devleker, MathWorks

HPC Advisory Council Stanford Conference
  Feb. 7-8, 2013, Stanford, Calif.
Open to the public
Note: NVIDIA's Sarah Tariq to present on scaling applications on large GPU clusters

Intro to CUDA + OpenACC Course
  Feb. 12-14, 2013, Ukraine, Lviv
Sponsored by Applied Parallel Computing LLC
Contact: akomissarov[at]nvidia[dot]com

Winter GPU Computing School (Moscow State University)
  Feb. 20-22, 2013, Moscow, Russia

Intro to CUDA + OpenACC Course
  March 4-5, 2013, Hamburg, Germany
Sponsored by Applied Parallel Computing LLC
Contact: akomissarov[at]nvidia[dot]com

4-Day CUDA Course - Oil & Gas (Acceleware)
  March 12-15, 2013, Houston, Texas
Instructor: Dr. Kelly Goss

HPC Advisory Council
  March 13-15, 2013, Lugano, Switzerland

GPU Tech Conference (GTC 2013)
  March 18-21, 2013, San Jose, Calif.
Special code for CUDA newsletter readers: GM10CD

4-Day CUDA Course (Acceleware)
  May 7-10, 2013, San Francisco, Calif.
Instructor: Dr. Kelly Goss

(To list an event, email: cuda_week_in_review@nvidia.com)

CUDA RESOURCES

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GPU-Accelerated Apps
List of 200+ popular GPU-accelerated scientific and research applications.

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

CUDA Education
Udacity Course
Coursera Course

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: cuda_week_in_review@nvidia.com (with CUDA Consulting in subject line).

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

Downloads
CUDA 5
CUDA 5 survey
Nsight

CUDA on the Web
CUDA Spotlights
CUDA Newsletters
CUDA Zone
GPU Test Drive
GPUComputing.net
GPGPU.org

ABOUT CUDA
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 and suggestions on the newsletter to cuda_week_in_review@nvidia.com
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