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CUDA Week in Review Newsletter
Wed., Aug. 28, Issue #99 Newsletter Home


A news summary for the worldwide CUDA, GPGPU and parallel programming community.
CUDA Pro Tip: The NVIDIA System Management Interface, or nvidia-smi, is a command-line interface to the NVIDIA Management Library, NVML. nvidia-smi provides Linux system administrators with powerful GPU configuration and monitoring tools. Watch the new CUDACast about nvidia-smi.


Pierre Wahl CUDA-Accelerated Photonics
This week’s Spotlight is on Pierre Wahl, a PhD student at the Vrije Universiteit Brussel in Belgium. As a member of the Brussels Photonics Team (B-PHOT), Pierre designs energy-efficient optical interconnects and collaborates with the NVIDIA Application Lab at Julich. Read our interview with Pierre Wahl.


Manal Jalloul Young Entrepreneur Award
Congrats to Manal Jalloul, winner of the ERSA Young Entrepreneur Award for her proposal titled "A Novel Parallel Computing Approach for Motion Estimation Based on Particle Swarm Optimization." Manal, a PhD student at the American University of Beirut, noted that "the NVIDIA GPU has evolved into a highly parallel, multithreaded, many-core processor with tremendous computational horsepower."

Compute the Cure
As part of its Compute the Cure initiative, the NVIDIA Foundation is awarding up to $200k to a selected cancer research project. The Foundation has issued a request for proposals for computational genomics or proteomics projects that aim to have a dramatic impact on the battle against cancer and reduce the time it takes for research outcomes to be effective in a clinical environment. Applications are due by Oct. 7. Learn more.

Open Alpha of PIConGPU
PIConGPU is a Particle-in-Cell (PIC) code running on GPUs. By providing insight into the interaction of light and matter, PIC codes support studies across many areas, from antenna research to plasma physics. The new version is full GPL open source and free for download to developers and power users. PIConGPU is developed by the Junior Group Computational Radiation Physics at the Institute of Radiation Physics at HZDR in collaboration with the Technical University Dresden.

HPCwire Readers Choice Nominations
HPCwire is accepting nominations for its annual Readers Choice Awards. Winners will be announced at SC '13 in November in Denver, Colorado. The deadline is Sept. 4.

GTC 2014 Call for Submissions
Call for Submissions for the GPU Technology Conference (March 24-27, 2014) is open through Sept. 27.

Upcoming Webinars
Sept. 5: Data Discovery through High-Data-Density Visual Analysis, Jan-Kees Bruenen, SynerScope
Sept. 10: Virtualizing Tough 3D Workloads with VMware and NVIDIA, Mike Coleman, VMware
Sept. 12: Guided Performance Analysis with NVIDIA Visual Profiler, David Goodwin, NVIDIA
Sept. 17: ArrayFire: Productive GPU Software Library for Defense, Kyle Spafford, AccelerEyes


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Subscribe to the Parallel Forall RSS feed Parallel Forall:
CUDACasts #7: nvidia-smi, by Mark Ebersole
CUDACasts #6: CUDA on ARM with CUDA 5.5, by Mark Ebersole

SQream Uses GPUs to Blast Through Big Data, by Alain Tiquet
In Vienna, Making Wood Waltz Down Stone Walls, by Jens Neuschafer


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NVIDIA seeks a parallel computing software engineer to join the developer technology team. In this role you will work with scientific application developers and collaborate with NVIDIA’s software and architecture teams. Strong knowledge of programming languages and mathematical fundaments required. Contact:

(Tip for job seekers: Update your LinkedIn profile. If you have experience with parallel programming and CUDA, add these terms to your "Skills and Expertise" section.)


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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)
  Sept. 2-3, 2013, Boston, Mass.

Data Discovery through High-Data-Density Visual Analysis (Webinar)
  Sept. 5, 2013
Presenter: Jan-Kees Bruenen, SynerScope

Parallel Processing and Applied Mathematics Conference
  Sept. 8-11, 2013, Warsaw, Poland

Guided Performance Analysis with NVIDIA Visual Profiler (Webinar)
  Sept. 12, 2013
Presenter: David Goodwin, NVIDIA

CUDA Programming Course (Delft University of Technology)
  Sept. 13, 2013, Delft, Netherlands

GROMACS USA Workshop and Conference
  Sept. 13-15, 2013, Charlottesville, Virginia

ArrayFire: Productive GPU Software Library for Defense (Webinar)
  Sept. 17, 2013
Presenter: Kyle Spafford, AccelerEyes

2-Day CUDA Training (AccelerEyes)
  Sept. 23-24, 2013, Washington, D.C

SPIE Conference on High-Performance Computing in Remote Sensing
  Sept. 23-26, 2013, Dresden, Germany.

4-Day CUDA Course (Acceleware)
  Sept. 24-27, 2013, Frankfurt, Germany


  Oct. 7-11, 2013, Recife, Pernambuco, Brazil
Instructors: A. Roiberg, R. Walker, S. LeGrand, R. Salomon-Ferrer

2-Day CUDA Training (AccelerEyes)
  Oct. 7-8, 2013, Houston, Texas

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

(To list an event, email:


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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-Accelerated Apps

List of 200+ popular GPU-accelerated scientific and research applications (PDF 402KB).

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.

CUDA Online Courses

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:

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


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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