CUDA: Week in Review
Friday, May 28, 2010, Issue #23 Newsletter Home

Welcome to "CUDA: Week in Review," a weekly newsletter for the worldwide CUDA and GPU Computing community. Contact us at: Follow us on Twitter:

GPU Technology Conference (GTC) Update:
- Proposal submissions are due June 1:
- Watch our GTC video on YouTube:
- Read more about GTC:
- Sign up for GTC email updates:
- Join the GTC LinkedIn group:
- See GTC 09 research posters:

Sharpen Your Skills in Summer School!

The Virtual School of Computational Science and Engineering is offering summer classes that can be taken online or at on-site locations across the U.S. The courses are designed for graduate students, post-docs and professionals from academia, government and industry. The schedule is:

  • Petascale Programming Environments and Tools, July 6-9, 2010
    • Includes presentation on "High-Performance Computing at the Petascale" as well as hands-on labs on visualization tools and other topics.
  • Big Data for Science, July 26-30, 2010
    • Instructors: Geoffrey C. Fox, distinguished scientist and director, and Judy Qiu, assistant director, Community Grids Lab, Pervasive Technology Institute, Indiana University.
  • Proven Algorithmic Techniques for Many-Core Processors, August 2-6, 2010
    • Instructors: Wen-mei Hwu, professor of electrical and computer engineering, University of Illinois at Urbana-Champaign and David Kirk, NVIDIA Fellow.
    • Note: The pre-requisite for this course is Introduction to CUDA.

For more info, see:

Northeastern University: Reducing Disease Diagnostics Time

The Laboratory for Spectral Diagnosis at Northeastern University, under the leadership of Prof. Max Diem, is focused on the spectral diagnosis of disease. The lab uses objective measurements and mathematical-statistical methods to diagnose diseases affecting human cells, tissues or body fluids. Using Jacket from AccelerEyes to accelerate the hyperspectral image analysis workflow, the lab has dramatically reduced the time for diagnoses with CUDA.
- Background: In hyperspectral imaging, sensors collect a set of images, each of which
  represents a range of the electromagnetic spectrum. The images are then combined to form
  a 3D hyperspectral cube for processing and analysis.
- Read more and watch the videos here:
- Note: In the second video, hear how a 7-hour non-parallelized project now takes only
  65 milliseconds with the GPU-based system.

New on CUDA Zone: CUDA for Neural Networks
Extract: "The NeuroSolutions CUDA add-on implements high-performance parallel computing of neural networks. Neural networks are a form of artificial intelligence (AI) that have proved to be effective in solving a wide range of data mining and data modeling problems including credit card fraud detection, cancer diagnosis and financial forecasting. As problems become more complex, so does the demand for processing power. By parallelizing advanced learning algorithms on a GPU, NeuroSolutions can achieve up to 50 times greater performance than that of a traditional CPU." By G. Lynn and B. Kachnowski, NeuroDimension, Inc. See:

CUDA Zone: Have a CUDA-related app or paper? Show it on CUDA Zone:
Reveal Imaging Technologies, a leader in the development of threat detection products, is looking for a hands-on junior engineering scientist to investigate new sensor technologies and their potential applications. Qualifications include: MS with concentration in computer science, math, physics, or other related engineering field; familiarity with signal processing; proficiency in MATLAB, LabView, C++, CUDA. Experience with infrared, mm-wave imaging, 3D surface rendering a plus. Location: Bedford, Mass. See: (job # 2010067)
CUDA Training

- GPU Programming with CUDA Fortran, C, PGI Accelerator
        June 3, Paris (presented by Michael Wolfe of the Portland Group in cooperation
        with SciWorks Technologies)

- CUDA training from Acceleware
        July 26-30, Cambridge, Mass: (with Microsoft)
        August 2-6, New York City: (with Microsoft)
        Sept. 13-17, Calgary:
        See press release:

- CUDA training from SagivTech
        CUDA course: July 12-14, Ra’anana, Israel
        GPU/Image Processing course: Aug. 2-4, Ra’anana, Israel

GPU Computing Webinars (CUDA C, OpenCL, Parallel Nsight and more…)
See webinar schedule:
CUDA and Academia
Over 350 universities are teaching CUDA and GPU Computing courses.
- See the list:

– ISC ´10 GPU Computing Workshops
May 30, Hamburg, Germany

– GPUs in Finance Roundtable
June 14, New York, NY (invitation only)

– European Association of Geoscientists & Engineers (EAGE) Conference
June 14-17, Barcelona

– Parallel Execution of Sequential Programs on Multi-Core Architectures
June 20, France

– GPGPU Briefing on Financial Services (Microsoft/NVIDIA)
June 21, NYC

– SIFMA Financial Services Tech Expo
June 22-24, New York, NY

– GPUs in Chemistry and Materials Science
June 28-30, Univ. of Pittsburgh

– Parallel Symbolic Computation 2010 (PASCO)
July 21-23, France

– Symposium on Chemical Computations on GPGPUs
Aug. 22-26, Boston

– Unconventional High Performance Computing 2010 (UCHPC 2010)
Aug. 31-Sept. 1, Italy

– GPU Technology Conference (GTC) 2010
Sept. 20-23, San Jose, Calif. (now accepting proposals from industry and academia)

– Supercomputing 2010
Nov. 13-19, New Orleans, LA

– IEEE International Parallel & Distributed Processing Symposium
May 16-20, 2011, Anchorage, AL

(To list an event, email:

CUDA Articles in Dr. Dobb’s
– Supercomputing for the Masses, Part 18:
– Supercomputing for the Masses, Part 17:
– Supercomputing for the Masses, Part 16:
– Supercomputing for the Masses, Part 15:
CUDA Books
– Programming Massively Parallel Processors by D. Kirk, W. Hwu:
– See additional books here:
CUDA Toolkit
Download CUDA Toolkit 3.0:
NVIDIA Parallel Nsight
Download the Parallel Nsight Beta:
CUDA Documentation
Download developer guides and documentation:
– Follow CUDA & GPU Computing on Twitter:
– Network with other developers:
– Stayed tuned to GPGPU news and events:
– Learn more about CUDA on CUDA Zone:
– CUDA on YouTube:
About CUDA:
CUDA is NVIDIA’s parallel computing hardware architecture. NVIDIA provides a complete toolkit for programming on the CUDA architecture, supporting standard computing languages such as C, C++, and Fortran as well as APIs such as OpenCL and DirectCompute.

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