| Tuesday, Oct. 26, 2010, Issue #39 - Newsletter Home |
| WELCOME | ||||||||||||||||||||||||||||||||||||||||||||||
| Welcome to CUDA: Week in Review, an online news summary for the worldwide CUDA and GPU computing community. | ||||||||||||||||||||||||||||||||||||||||||||||
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| CONTENTS | ||||||||||||||||||||||||||||||||||||||||||||||
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| CUDA SPOTLIGHT | ||||||||||||||||||||||||||||||||||||||||||||||
| 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: http://is.gd/gaorM | ||||||||||||||||||||||||||||||||||||||||||||||
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| CUDA NEWS | ||||||||||||||||||||||||||||||||||||||||||||||
| 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: www.ks.uiuc.edu/Research/namd/2.7/announce.html |
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| CUDA ZONE | ||||||||||||||||||||||||||||||||||||||||||||||
| 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: http://is.gd/gdkww |
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| CUDA JOB OF THE WEEK | ||||||||||||||||||||||||||||||||||||||||||||||
| 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: www.tmvse.com/Recruitment/Rendering-Scientist/ |
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| CUDA CALENDAR | ||||||||||||||||||||||||||||||||||||||||||||||
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2010: – Beginner CUDA Seminar - empulse GmbH
– GPUs for Molecular Dynamics/GROMACS
– NEW: Debugging at Scale (webinar) - Allinea Software
– NEW: Debugging at Scale (webinar) - Allinea Software
– NEW: Boost Your Productivity with GPUs (webinar) – Platform Computing and HP
– NEW: HPC China 2010
– Beginner CUDA Course - SagivTech
– NEW: GPU Computing Forum on Linear Algebra Software for GPUs (webinar)
– NEW: Analytics & Risk Technology in Finance - Wolfram Research
– NEW: Debugging GPUs (webinar) - Allinea Software
– Supercomputing 2010 (SC10)
– NEW: GPU Programming with CUDA Fortran, CUDA C, PGI Accelerator - PGI
– Advanced GPU Supercomputing for High-Frequency Trading
– Training from CAPS
– NEW: Improve Time to Debug (webinar) - Allinea Software
– NEW: MATLAB Expo
– NEW: Call for Papers - IEEE/ACM Intl. Symposium on Cluster, Cloud and Grid Computing
– CUDA and Advanced Image Processing - SagivTech
– SIGGRAPH Asia
2011: – Scientific Computing in the Americas: The Challenge of Massive Parallelism
– IEEE International Parallel & Distributed Processing Symposium
Ongoing
(To list an event, email: cuda_week_in_review@nvidia.com) |
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| CUDA RESOURCES | ||||||||||||||||||||||||||||||||||||||||||||||
| GPU Technology Conference | ||||||||||||||||||||||||||||||||||||||||||||||
| – See presentations and keynotes from GTC 2010: www.nvidia.com/gtc | ||||||||||||||||||||||||||||||||||||||||||||||
| CUDA GPUs | ||||||||||||||||||||||||||||||||||||||||||||||
| – See list of CUDA-enabled GPUs: www.nvidia.com/object/cuda_gpus.html | ||||||||||||||||||||||||||||||||||||||||||||||
| CUDA Downloads | ||||||||||||||||||||||||||||||||||||||||||||||
| – Download CUDA Toolkit 3.2: http://bit.ly/aKCENp – Download OpenCL v1.1 pre-release drivers and SDK code samples (Log in or apply for an account) |
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| CUDA Documentation | ||||||||||||||||||||||||||||||||||||||||||||||
| – Get developer guides and docs: http://developer.nvidia.com/object/gpucomputing.html | ||||||||||||||||||||||||||||||||||||||||||||||
| CUDA and Academia | ||||||||||||||||||||||||||||||||||||||||||||||
| – Learn more at http://research.nvidia.com/ | ||||||||||||||||||||||||||||||||||||||||||||||
| CUDA on the Web | ||||||||||||||||||||||||||||||||||||||||||||||
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– Read previous issues of CUDA: Week in Review: http://is.gd/cBXbg – Follow CUDA & GPU Computing on Twitter: www.twitter.com/gpucomputing – Network with other developers: www.gpucomputing.net – Stayed tuned to GPGPU news and events: www.gpgpu.org – Learn more about CUDA on CUDA Zone: www.nvidia.com/cuda – Read Kudos for CUDA: http://www.hpcwire.com/features/Kudos-for-CUDA-97889444.html – Read Supercomputing for the Masses, Part 20: http://is.gd/f9o6o |
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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: cuda_week_in_review@nvidia.com | ||||||||||||||||||||||||||||||||||||||||||||||
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| Stay in Touch with NVIDIA | ||||||||||||||||||||||||||||||||||||||||||||||
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