CUDA: Week in Review
Friday, June 4, 2010, Issue #24 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: See previous CUDA: Week in Review issues:

Update on GPU Technology Conference (GTC) 2010
– Registration opened this week for GTC 2010, which takes place on Sept. 20-23 in
   Silicon Valley.
– When you sign up, enter GMCUDANEWS10 for a 10% discount (special code for readers
   of CUDA: Week in Review).
All Eyes on Hamburg and the TOP500
This was a big week for supercomputing! The International Supercomputing Conference (ISC ´10) was held in Hamburg, Germany and attracted a record number of attendees. A closely-watched event at ISC is the announcement of the annual "TOP500" list of the world´s fastest supercomputers. This year, two new NVIDIA GPU-based systems achieved a ranking on the TOP500 list:
 – The Nebulae supercomputer at the National Supercomputing Center in Shenzhen, China
    is #2.
 – The supercomputer at the Institute of Process Engineering, Chinese Academy of Sciences
    is #19.
Both systems are based on NVIDIA Tesla. Tesla also powers a third supercomputer on the TOP500, Tokyo Tech’s TSUBAME 1.2. See the TOP500 press release:
CUDA in Moscow
A CUDA/Tesla conference was held at Moscow State University featuring presentations on biophysics, biochemistry, molecular dynamics and genetics, drawing over 100 participants. Andy Keane, NVIDIA’s Tesla General Manager, was a guest speaker. NVIDIA partner Applied Parallel Computing participated.
– For more info on Applied Parallel Computing, see:
– To purchase a CUDA book in Russian, see:
Test Your Parallel Programming Skills in Grenoble!
Join the computer algebra/parallel programming contest organized by PASCO in July in Grenoble, France. Be prepared to face other participants in real time! Participation is open to people having a background in computer algebra and/or parallel computing (letter of intent due June 13).
– See:
New on CUDA Zone: GPU Algorithm for Level Set Segmentation
Application Domain: Medical Imaging
Authors: M. Roberts, J. Packer, M.C. Sousa, J. R. Mitchell; University of Calgary, Canada Extract: "Identifying distinct regions in images - a task known as segmentation - is an important task in computer vision and medical imaging. The ‘level set’ method is a powerful technique for image segmentation under challenging conditions…. In this paper we describe a new GPU algorithm that dramatically improves computational efficiency without affecting accuracy…. We apply our algorithm to 3D medical images and we demonstrate that in typical clinical scenarios, our algorithm is 14X faster than previous algorithms…. We implemented our algorithm using CUDA and the CUDA Data Parallel Primitives Library." Note: Paper to be presented at High-Performance Graphics 2010, June 25-27, Saarbrucken, Germany. See:
CUDA Zone: Have a CUDA-related app or paper? Post it on CUDA Zone: and receive a CUDA t-shirt!
Imperial College London is offering postdoc positions related to computational science on parallel, multicore and manycore/GPGPU platforms. This is an opportunity to take a leadership role in a multidisciplinary collaboration involving applications such as ocean circulation modeling, bone implant biomechanics, urban pollution modeling and dam stability. A relevant Ph.D. or strong research experience is essential.
– Background on the research group:
– Read more:
CUDA Research and Certification
– NEW: NVIDIA launches programs for GPU Computing developers. For more info, see:
GPU Computing Webinars (CUDA C, OpenCL, Parallel Nsight and more…)
– NEW: See June webinar schedule:
CUDA Training
– CUDA training from Acceleware
July 26-30, Cambridge, Mass: (with Microsoft)
August 2-6, New York City: (with Microsoft)
Sept. 13-17, Calgary:

– CUDA training from SagivTech
CUDA course: July 12-14, Ra’anana, Israel
GPU/Image Processing course: Aug. 2-4, Ra’anana, Israel
CUDA Classes
– Virtual School of Comp. Science & Engineering
Proven Algorithmic Techniques for Many-Core Processors: Aug. 2-6, 2010
CUDA and Academia
Over 350 universities are teaching CUDA and GPU Computing courses.
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, Saint-Malo, France

GPGPU Briefing on Financial Services (Microsoft/NVIDIA)
June 21, New York, NY

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

High Performance Graphics 2010
June 25-27, Saarbrucken, Germany

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

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

July 25-29, Los Angeles

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:
CUDA Documentation
Download developer guides and documentation:
NVIDIA Parallel Nsight
Download the Parallel Nsight Beta:
– Read previous issues of CUDA: Week in Review:
– Follow CUDA & GPU Computing on Twitter:
– Network with other developers:
– Stayed tuned to GPGPU news and events:
– Learn more about CUDA on CUDA Zone:
– Watch 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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