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CUDA Week in Review Newsletter
Wed., Oct. 16, 2013, Issue #102 Newsletter Home
Welcome to CUDA: Week In Review, news for the worldwide CUDA, GPGPU and parallel programming community.
CUDA Pro Tip: Like Python? Try NumbaPro, a Python compiler from Continuum Analytics. It compiles Python code for execution on CUDA-capable GPUs. Read about it on Parallel Forall.


Cyrille Favreau and Christophe FavreauCUDA-Accelerated Adventures
This week’s CUDA Spotlight is on Cyrille Favreau and Christophe Favreau, brothers who leverage GPU computing in different ways, with equally compelling results.

Cyrille, a technical architect by day, uses CUDA in his free time to pursue his interest in visualization technologies. His projects include building a real-time ray-tracing engine and molecule visualizer, and exploring fractal theory.

Christophe, a professional photographer and videographer, is passionate about sailing and nature. GPUs help him produce beautiful work as he travels the world.


CUDA Book New CUDA Book
Congrats to NVIDIANs Gregory Ruetsch and Massimiliano Fatica, who have authored a new book: CUDA Fortran for Scientists and Engineers: Best Practices for Efficient CUDA Fortran Programming (Morgan Kaufmann). The book is designed to show how high-performance application developers can leverage the power of GPUs using Fortran.

Tesla M2090 Redux
Looking for an entry-level Tesla GPU accelerator? Want to expand your existing M2090 cluster? The Tesla M2090 is back by popular demand. At the same price point as a popular CPU, it easily achieves 2X the performance for many applications. Contact a Tesla Preferred Partner.

Tell Us Your CUDA Story
At SC13, NVIDIA will highlight CUDA stories from around the world. If you are a CUDA developer, tell us how you are using CUDA in 140 characters or less. Your submission will be considered for display in the NVIDIA booth and/or on the NVIDIA website. Respond here.

Upcoming Webinars
Oct. 22: Intro to SeqAn, an Open-Source C++ Template Library, Knut Reinert, FU Berlin
Oct. 23: Revolutionize Virtual Desktops..., Will Wade, NVIDIA
Oct. 29: Improve Performance using CUDA Memory Model, Kelly Goss, Acceleware
Oct. 30: OpenACC 2.0 Enhancements for Cray Supercomputers, James Beyer, Cray Inc.
Nov. 5: Accelerating Face-in-the-Crowd Recognition, Brian Lovell, Stephen Brain, Imagus Technology
Nov. 6: Bright Cluster Manager Solution for GPU-based HPC, Ian Lumb, Bright Computing

Upcoming Meetups
Oct. 17: Singapore (New!)
Oct. 17: Paris
Oct. 17: Sydney
Oct. 22: Boston
Oct. 24: Brisbane
Oct. 29: Minneapolis
Nov. 4: Silicon Valley
Nov. 20: Colorado (Co-located with SC13)


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Subscribe to the Parallel Forall RSS feed Parallel Forall:
CUDACasts Episode #10: Accelerate Python on GPUs, by Mark Ebersole
Subscribe to NVIDIA RSS feed NVIDIA:
Using GPUs to Help Weave the Next Invisibility Cloak, by Brian Caulfield
GPUs Further Russia's Supercomputing Efforts, by George Millington


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TU Darmstadt, a leading technical university in Germany, is offering a full professorship in parallel programming. This position is intended to further strengthen TU Darmstadt’s competence in high-performance computing. Relevant topics include new paradigms of parallel programming; methods for scalable libraries; and tools for performance analysis.


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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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2-Day CUDA Training (AccelerEyes)
  Oct. 21-22, 2013, Atlanta, Georgia

Intro to SeqAn, an Open-Source C++ Template Library (Webinar)
  Oct. 22, 2013
Presenter: Knut Reinert, FU Berlin

CUDA Seminar (NVIDIA Korea)
  Oct. 24, 2013, Seoul, S. Korea
Note: Lunch will be provided

CUDA and Open ACC Workshop (TU Kaiserslautern)
  Oct. 29, 2013, Kaiserslautern, Germany

Improve Performance using CUDA Memory Model (Webinar)
  Oct. 29, 2013
Presenter: Kelly Goss, Acceleware

OpenACC 2.0 Enhancements for Cray Supercomputers (Webinar)
  Oct. 30, 2013
Presenter: James Beyer, Cray Inc.

2-Day CUDA Training (University of Cambridge)
  Oct. 31-Nov. 1, 2013, Cambridge, England


2-Day CUDA Training (AccelerEyes)
  Nov. 4-5, 2013, Baltimore/Washington DC

4-Day CUDA Course (Acceleware)
  Nov 12-15, 2013, Houston, Texas

SC ’13
  Nov. 17-22, 2013, Denver, Colorado

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. 23-26, 2013, Seoul, Korea
Note: IEEE Workshop

(To list an event, email:


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Check out our new series of short videos about CUDA.

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


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
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GPU Test Drive

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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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