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CUDA Week in Review Newsletter
Thurs., April 10, 2014, Issue #109 Newsletter Home
Welcome to CUDA: Week In Review
News and resources for the worldwide GPU and parallel programming community.
Thank you to everyone who participated in GTC 2014. During four action-packed days in San Jose, 550 talks and 175 posters were presented, along with 50+ developer labs and tutorials and 40 hangout sessions. Content is available online now for registrants and will be available to all in late April. In the meantime, you can view the keynotes here and posters here.


Did you know you can increase performance with NVIDIA GPU Boost? GPU Boost makes use of any power headroom to improve application performance. Learn all about it on the Parallel Forall Blog.


Paul RichmondCUDA-Accelerated Simulation
This week’s Spotlight is on Paul Richmond, a Vice Chancellor’s Research Fellow at the University of Sheffield (a CUDA Research Center). Paul’s research interests relate to the simulation of complex systems and parallel computer hardware. He is the developer of FLAME GPU (Flexible Large-scale Agent Modeling Environment for the GPU). Read the Spotlight.


Global Impact Award Announced
The NVIDIA Global Impact Award was announced at GTC. The Award is an annual grant of $150,000 for groundbreaking work using NVIDIA technology to address the world's most important social and humanitarian problems. Researchers, non-profit institutions and universities based anywhere in the world may apply at Nominations are due Dec. 12, 2014.

New PGI Product
PGU has released Free PGI for OS X. It includes the PGI high-performance parallel C99 and Fortran 2003 compilers and parallel debugger for 64-bit and 32-bit Intel processor-based Macs.


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April 22: Large-scale DFT Calculations on GPUs, H. Kulik, MIT
May 13: Overview of AMBER 14, R. Walker, UCSD and S. Le Grand, Amazon Web Services
June 3: Next Steps for Folding@home, V. Pande, Stanford


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Subscribe to the Parallel Forall RSS feed Parallel Forall:
Jetson TK1: Mobile Embedded Supercomputer Takes CUDA Everywhere, M. Harris
NVLink, Pascal and Stacked Memory: Feeding the Appetite for Big Data, D. Foley
Subscribe to NVIDIA RSS feed NVIDIA:
Matter Matters: Understanding Anti-Matter with the Help of GPUs, T. Kontzer


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4-Day CUDA Course (Acceleware)
  May 6-9, 2014, Calgary, AB, Canada

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

PRACE Scientific and Industrial Conference
  May 20-22, 2014, Barcelona Spain

1-Day CUDA Course (Delft University of Technology)
  June 5, 2014, Delft, Netherlands
Instructors: Kees Vuik & Kees Lemmens

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

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

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

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

(To list an event, email:


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Online Learning

Udacity | Coursera | APC Russia

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

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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.



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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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