CUDA: Week in Review
Tue., November 1, 2011, Issue #64 - Newsletter Home
Welcome to the online newsletter for the worldwide CUDA, GPGPU and parallel programming ecosystem.
Dr. Jeffrey Vetter, Oak Ridge National Laboratory
NVIDIA GPU Tech Theater at SC11
Oak Ridge National Lab and Titan
GTC Submission Deadline
Cornell Launches Red Cloud
Congrats to Francesco Rossi
Video for Geo-Spatial Awareness
Sign up to be a CUDA Registered Developer
Follow @GPUComputing on Twitter
CUDA Spotlight
GPU-Accelerated Real Science
This week’s Spotlight is on Dr. Jeffrey Vetter of Oak Ridge National Laboratory and Georgia Tech. Here’s an extract from our interview.
Matt Thompson, Bill Putman, Max Suarez
NVIDIA: Jeff, what is the focus of your work?
Jeff: Over the last decade, I have been investigating hardware and software technologies that will most likely appear in future supercomputer systems, and which of those technologies best satisfy specific application workloads. I have worked on a number of projects: IBM BlueGene/L, Cray X1, Cray XT, FPGAs, GPUs and other technologies. Our team’s early work has contributed to the design and deployment of the NSF Keeneland system and the DOE Titan system.

Not surprisingly, our work over the last several years has primarily focused on GPUs. Our team is involved in most every aspect of GPUs in computational science: future architectures, programming systems, applications development, and education and outreach. For example, we are partners on NVIDIA’s Echelon research project, which is sponsored by the DARPA UHPC program with the goal of fitting one PetaFLOPS in one rack and using less than 57K watts of power….
NVIDIA: What are the key drivers in supercomputing today?
Jeff: From the facility or data center perspective, the key driver is the energy required for running large scale supercomputers. This is not just a prediction. Look at most contemporary supercomputing facilities: they are often limited by the amount of power that can be physically delivered to the computer in the building….

On the other hand, applications developers are most concerned about programmability. The last significant transition for the scientific computing community was the transition in the 1990s from vector computing to distributed memory computing with MPI. In order to provide solutions to these questions, our team is investigating multiple fronts: CUDA, compiler directives, runtime libraries, frameworks and debugging and correctness tools. It is an exciting time to be in computer science!
NVIDIA: How does CUDA fit into the modern computing landscape?
Jeff: CUDA is a phenomenon. In less than five years, the CUDA programming model has grown from its initial introduction to wide adoption. It is easy to forget how challenging it was to program GPUs prior to CUDA. These days, CUDA is so pervasive that many students get their first introduction to parallel programming and fine-grained parallelism with CUDA on their laptop GPU….
  - Read the complete interview with Jeff Vetter

  (To suggest a CUDA Spotlight, email
CUDA Developer News
NVIDIA GPU Technology Theater at SC11 back to the top
Going to SC11 in Seattle? If so, drop by the GPU Technology Theater at the NVIDIA booth (#2719). The Theater will feature talks from industry experts including Jack Dongarra, University of Tennessee; Patrick McCormick, Los Alamos National Laboratory; Richard Brower, Boston University; and Satoshi Matsuoka, Tokyo Institute of Technology. For the complete speaker lineup, visit the NVIDIA SC11 event page. The talks will be streamed live via Facebook at

Oak Ridge National Lab to Deploy Titan
Oak Ridge National Laboratory (ORNL), which operates the world's premier open science computing facility for the U.S. Department of Energy, will deploy a new supercomputer called Titan, based on Tesla GPUs. The system, which is referenced in today's CUDA Spotlight with Dr. Jeff Vetter, will be used across fields such as materials science, energy technology, medical research and more.
- See the VentureBeat interview with NVIDIA's Steve Scott (8 mins.):

GTC U.S. Submission Deadline
If you (or someone you know) is interested in speaking at GTC U.S. (May 2012), proposals must be submitted by Thurs., Nov. 3, 2011. Details can be found on the GTC website.
- To make a submission, visit:

Cornell Launches Red Cloud
The Cornell University Center for Advanced Computing (CAC) has launched Red Cloud, an on-demand research supercomputing service available by subscription. The basic offering, called "Red Cloud," is an Infrastructure as a Service (IaaS) that runs Eucalyptus, the open source cloud computing platform. The second offering, "Red Cloud with MATLAB," is a Software as a Service (SaaS) that runs MATLAB Distributed Computing Server and features NVIDIA GPUs. Red Cloud services run on Dell PowerEdge C servers.
- Visit

Congratulations to Francesco Rossi
Congratulations to Francesco Rossi, University of Bologna, on his recent master’s thesis titled: "Development of Algorithms for an Electromagnetic Particle in Cell Code…." While completing his thesis, Francesco developed Jasmine, a flexible CPU+GPU PIC (particle in cell) framework to assist in the design of laser-plasma accelerators. Jasmine runs PIC 3D simulations on multi-GPU clusters, such as CINECA’s Tesla-powered PLX, gaining large speedups.
- Read more:

Intergraph: Video for Geo-Spatial Awareness
At the recent GeoINT trade show, Intergraph announced a new release of its GeoMedia Motion Video Analyst solution with CUDA acceleration. GeoMedia Motion Video Analyst speeds the process of creating images from full motion video (FMV). This feature improves the ability of defense analysts to maintain situational awareness and act quickly on intelligence data.
- For info, see:
New on the NVIDIA Blog
back to the top
Webinar: Speed Up MATLAB With NVIDIA GPUs, by Dan Doherty, MathWorks
NVIDIA Engineering Strike Force, by Brian Kelleher
NVIDIA CEO Wraps Up All Things D Asia Event, by Bob Sherbin
Replays of the Week
back to the top
NEW: Each week we highlight a session from a GPU Technology Conference event. Here is our pick for this week: Computer Vision on GPU with OpenCV (Israel 2011) by Anton Obukhov, NVIDIA
- For info, see:
back to the top
NEW: GE Healthcare is seeking a software engineer to specify, design and implement software for a new X-ray acquisition system. Desired characteristics include experience with GPU/CUDA and imaging software.
- See:
(To submit a job listing, email
back to the top
The GPU Meetups offer a great way to learn about GPU computing and meet interesting people in a relaxed environment. Please feel free to attend any of these upcoming Meetups:

GPU Meetup of Boston, Thurs., Nov. 3 at 6:00 pm
Topic: HPC and Protein Simulation, Francesco Pontiggia, Brandeis University
GPU Meetup of Silicon Valley, Mon., Nov. 7 at 6:15 pm
Topic: GPU Programming with Thrust and Copperhead, Bryan Catanzaro, NVIDIA
GPU Meetup of Seattle, Wed., Nov. 16, 7:00 pm (networking), 7:45 pm (program)
Special SC11 Meetup! Talks by NVIDIA, Microsoft, LexisNexis. Location: Amazon
GPU Meetup of New Mexico, Wed., Nov. 16, 7:00 pm
Topic: ROMIO and MPI-IO in Hybrid HPC
GPU Meetup of Brisbane, Nov. 17, 6:00 p.m
GPU Meetup of New York City, Nov. 21, 6:00 pm

(Would you like to start a Meetup? Email
back to the top
GTC Asia 2011 (Dec. 14-15)
Poster deadline: Nov. 3

GTC U.S. 2012 (May 14-17)
Session deadline: Nov. 3
Poster deadline: Dec. 8
CUDA Calendar
November 2011 back to the top
- NEW: Accelerate Science to Treatment with the MD SimCluster (Webinar)
Nov. 8, 2011
Sponsored by QLogic, Dell, NVIDIA

- NEW: 2-Day "Deep CUDA" Training - NOVATTE
Nov. 9-10, 2011, Singapore
Sponsored by NOVATTE, A*Star and NVIDIA
For info, email to Oxana Plis, op (at)

- Supercomputing 2011 (SC11)
Nov. 12-18, Seattle, Washington
Learn about NVIDIA activities at SC11:
For more information on SC11, visit
Join the GPU Technology Theater from your desk via the SC11 Facebook live stream:

- GPU Programming for Defense/Intelligence — AccelerEyes (Webinar)
Nov. 15, 2011
Learn to accelerate common defense and intelligence algorithms using easy, powerful programming libraries, with Jacket for use with MATLAB and LibJacket for C/C++/Fortran.

- NEW: Heterogeneous Data-Parallel Programming (Webinar)
Nov. 16, 2011
Presenter: Prof. Satnam Singh, University of Birmingham, U.K.

- CUDA Training (Basic and Advanced) — CAPS
Nov. 22-24, 2011, Rennes, France
Email: training (at)

- NEW: CUDA 4-Day Training Course – Acceleware
Nov. 22-25, 2011, Frankfurt, Germany
Presented by Acceleware with Microsoft
Instructor: Michael Durocher

December 2011

- AGU (American Geophysical Union) Meeting
Dec. 5, 2011, San Francisco
Session on High-Res Modeling Using GPU and Many-Core Architectures

- NEW: Intro to GPU Programming Workshop - La Maison de la Simulation
Dec. 5-9, 2011, France

- GTC Asia
Dec. 14-15, 2011, Beijing, China
Featuring the latest GPU computing breakthroughs, demos and presentations.

- LibJacket CUDA Library for Maximus — AccelerEyes (Webinar)
Dec. 15, 2011
Learn to integrate computations with visualizations in a CUDA-based app through simple visualization functions for plotting, image and volume rendering, and more.

(To list an event, email:

CUDA Resources
Tesla MD SimCluster back to the top
– Want to test drive a GPU? Try the Tesla Molecular Dynamics SimCluster:
– CUDA 4.0:
– Parallel Nsight:
– Parallel Nsight:
CUDA Registered Developer Program
– Sign up:
– List of CUDA-enabled GPUs:
CUDA on the Web
– See 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:
– Check out the NVIDIA Research page:
CUDA Recommended Reading
– Future of Computing Performance:
– Supercomputing for the Masses, Part 21:
– CUDA books:
CUDA Recommended Viewing
– The Third Pillar of Science:
– GTC 2010 presentations:
– SC10 presentations:
About CUDA
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 as well as APIs such as OpenCL and DirectCompute. Send comments and suggestions to:
Click here to opt in specifically to CUDA: Week in Review. NVIDIA
Copyright © 2011 NVIDIA Corporation. All rights reserved. 2701 San Tomas Expressway, Santa Clara, CA 95050.