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CUDA Week in Review Newsletter
Thur., June 12, 2014, Issue #112 Newsletter Home
Welcome to CUDA: Week In Review
News and resources for the worldwide GPU and parallel programming community.


Learn about the performance implications of the Tail Effect and how to work around it on the Parallel Forall Blog.


Dr. Mark Bathe GPU-Accelerated Nanotechnology
This week's Spotlight is on Dr. Mark Bathe, associate professor of biological engineering at the Massachusetts Institute of Technology. Mark's lab focuses on in silico design and programming of synthetic nucleic acid scaffolds.

Mark comments: "GPU computing plays a major enabling role in diverse aspects of our technology developments." Read the Spotlight.


Stanford Launches the HIVEStanford Launches the HIVE
Stanford University announced the HIVE, a state-of-the-art facility for scientific visualization in the Huang Engineering Center.

"Researchers are creating tremendous amounts of data through computations, simulations, measurements, sensor readings and so forth. We have to have a way to visualize such data in ways that allow us to see the big picture and also zoom in on the detail," said Dr. Margot Gerritsen, director of Stanford's Institute for Computational and Mathematical Engineering (ICME).

The HIVE consists of 35 high-definition displays, synched with NVIDIA technology, that can work together to offer a detailed view of one image, or display side-by-side visualizations of different images.

New Image & Signal Processing Research
Solutions provider SagivTech collaborated with the University of Bremen (Germany), EPFL (Switzerland) and others on the UNLoCX project. Funded by the European Commission, UNLoCX conducted numerical experiments with mass spectrometry imaging data on GPUs, reducing processing times from several hours to several minutes. The goal is to tackle complex applications in life sciences and ultra-precise audio signal processing which presently cannot be solved appropriately with existing algorithms. The results are described in a paper recently published in Advances in Computational Mathematics (June 2014).


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July 16: GPU Architecture & the CUDA Memory Model, C. Mason, Acceleware
July 22: GPU Computing with MATLAB, D. Doherty, MathWorks
Aug. 12: Asynchronous Operations & Dynamic Parallelism in CUDA, D. Cyca, Acceleware


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June 16: Silicon Valley
June 26: Brisbane, Australia
June 30: Riga, Latvia
July 10: Singapore


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Subscribe to the Parallel Forall RSS feed Parallel Forall:
Drop-in Acceleration of GNU Octave, N. Markovsky
CUDA Pro Tip: Minimize the Tail Effect, J. Demouth
Accelerating a C++ CFD code with OpenACC, J. Kraus


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ISCA ’14 (Symposium on Computer Architecture)
  June 14-18, 2014, Minneapolis, Minn.
(OpenCV + GPU tutorial on June 15, chaired by Dan Connors, Univ. of Colorado, with keynote by Steve Keckler, NVIDIA)

Intro to GPGPU Programming (VSCSE/XSEDE)
  June 16-20, 2014
(Virtual course delivered at multiple locations)

4-Day GPU Computing Course (SagivTech)
  June 22-25, 2014, Ramat Gan, Israel

ISC ’14
  June 22-26, 2014, Leipzig, Germany

HPDC ’14
  June 23-27, 2014, Vancouver, Canada

HPC Workshop: Summer Bootcamp (XSEDE)
  June 24-27, 2014
(Virtual course delivered at multiple locations)

4-Day CUDA Course (Acceleware)
  June 24-27, 2014, San Jose, Calif.


GPU Tech Workshop Taiwan (NVIDIA)
  July 8, 2014, Taipei, Taiwan

GPU Tech Workshop SE Asia (NVIDIA)
  July 10, 2014, Singapore

Programming Heterogeneous Systems in Physics (Workshop)
  July 14-15, 2014, Jena, Germany

GPU Tech Conference Japan (NVIDIA)
  July 16, 2014, Tokyo, Japan

GPUs and Scientific Applications (University of Alicante)
  July 21-24, 2014, Alicante, Spain

CUDA Programming Course (Oxford University)
  July 21-25, 2014, Oxford, UK
Instructor: Mike Giles

HPCS 2014
  July 21-25, 2014, Bologna, Italy

(To list an event, email:


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Online Learning

Udacity | Coursera | APC Russia

GPU-Accelerated Libraries

Adding GPU acceleration to an application can be as easy as calling a library function. Check out these high-performance libraries from NVIDIA and partners.

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 Test Drive

Want to try Tesla K40 for free? Sign up here.


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.

GPU-Accelerated Apps

See updated list of 270+ GPU-accelerated applications.

GPU Meetups

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

CUDA Documentation

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

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.

GPU Computing on Twitter

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



CUDA on the Web

CUDA Spotlights
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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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