CUDA: Week in Review
Tuesday, Oct. 26, 2010, Issue #39 - Newsletter Home
Welcome to CUDA: Week in Review, an online news summary for the worldwide CUDA and GPU computing community.
Dell published a new paper titled "Expanding the Boundaries of GPU Computing," which includes a case study about the National Center for Supercomputing Applications (NCSA) at the University of Illinois. The case study describes Lincoln - a 47 TFLOPS cluster based on Dell hardware with NVIDIA Tesla GPUs for parallel processing. NCSA’s John Towns says that NCSA is seeing "applications that on a per-GPU basis have an equivalent performance of anywhere from 30 to 40 CPU cores all the way up to over 200 CPU cores…."
  - Read the full story:
NAMD 2.7 Molecular Dynamics Code is GPU-Accelerated
In related news, the University of Illinois announced the public release of NAMD 2.7 (NAnoscale Molecular Dynamics), a popular molecular dynamics code for simulation of large biomolecular systems. The new version leverages GPU acceleration on a cluster, with each GPU providing the performance of 12 CPU cores. NAMD is distributed free of charge and includes source code. NAMD development is supported by the NIH National Center for Research Resources. Stay tuned for additional info.
  - See:
New on CUDA Zone: Realtime Tracking with a Pan-Tilt Camera – University of Massachusetts
Extract: "The human eye is amazingly adept at tracking moving objects. The process is so natural to humans that it happens without any conscious effort. While this remarkable ability depends in part on the human brain’s immense processing power, the fast response of the extraocular muscles and the eyeball’s light weight are also vital. Even a small point and shoot camera is typically too heavy and slow to move with the agility of the human eye. How, then, can we give a computer the ability to track movement quickly and responsively?

Thanks to recent progress in camera miniaturization, small, easily manipulable cameras are now readily available. In this project, we use a first person view (FPV) camera intended for use on model airplanes. The camera’s tracking software is written for an NVIDIA GPU using CUDA."

Authors: Blake Foster, Rui Wang, Erik Learned-Miller; Univ. of Massachusetts Computer Graphics and Computer Vision Labs. See:
Toshiba Medical Visualization Systems is seeking a Rendering/Visualization Scientist to join an Edinburgh-based team responsible for developing industry-leading 3D medical imaging technology. Candidates should have strong software engineering skills and ability to develop high-performance code in a test-driven environment. Experience in C/C++ required; GPU/GPGPU programming skills a plus.
  - See:

Beginner CUDA Seminar - empulse GmbH
Oct. 26, Cologne, Germany

GPUs for Molecular Dynamics/GROMACS
Oct. 28-29, Espoo, Finland

NEW: Debugging at Scale (webinar) - Allinea Software
Oct. 27, 10:00 a.m. BST (by David Lecomber, CTO, Allinea)

NEW: Debugging at Scale (webinar) - Allinea Software
Oct. 28, 8:00 a.m. pacific (by David Lecomber, CTO, Allinea)

NEW: Boost Your Productivity with GPUs (webinar) – Platform Computing and HP
October 27, 11:00 a.m. pacific

NEW: HPC China 2010
Oct. 27-30, Beijing (NVIDIA is a Diamond Sponsor)

Beginner CUDA Course - SagivTech
Oct. 31-Nov. 2, Ramat Gan, Israel

NEW: GPU Computing Forum on Linear Algebra Software for GPUs (webinar)
Nov. 3, 10:00 a.m. central (by Dr. Jack Dongarra)

NEW: Analytics & Risk Technology in Finance - Wolfram Research
Nov. 4, London

NEW: Debugging GPUs (webinar) - Allinea Software
Nov. 10, online

Supercomputing 2010 (SC10)
Nov. 13-19, New Orleans

NEW: GPU Programming with CUDA Fortran, CUDA C, PGI Accelerator - PGI
Nov. 15, New Orleans/SC10 (by Michael Wolfe, PGI)

Advanced GPU Supercomputing for High-Frequency Trading
Nov. 15-17, New York (by Andrew Sheppard)

Training from CAPS
Nov. 23-25, Rennes, France

NEW: Improve Time to Debug (webinar) - Allinea Software
Nov. 24-25, online

Nov. 26, Tokyo

NEW: Call for Papers - IEEE/ACM Intl. Symposium on Cluster, Cloud and Grid

Papers deadline: Nov. 30

CUDA and Advanced Image Processing - SagivTech
Dec. 12-14, Ramat Gan, Israel

Dec. 16-18, Seoul


Scientific Computing in the Americas: The Challenge of Massive Parallelism
Jan. 3-14, 2011, Valparaiso, Chile

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

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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. Send comments and suggestions to:
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