CUDA: Week in Review
Friday, June 11, 2010, Issue #25 - Newsletter Home
Welcome to "CUDA: Week in Review," a weekly newsletter for the worldwide CUDA and GPU Computing community.
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Update on GTC 2010 (GPU Technology Conference)
– We are pleased to welcome Adobe, HP, Microsoft, PNY, Supermicro and Dell as GTC
   2010 Platinum Sponsors, and Cooley LLP as a GTC 2010 Emerging Companies Summit
– GTC 2010 registration is now open!
– Special code for "CUDA: Week in Review" readers - GMCUDANEWS10
GPU Computing - At the Tipping Point
IEEE Computer Society has published an informative article about GPU Computing by NVIDIA's John Nickolls and Bill Dally. Here's the introduction:

"GPU computing is at a tipping point, becoming more widely used in demanding consumer applications and high-performance computing. This article describes the rapid evolution of GPU architectures (from graphics processors to massively parallel many-core multiprocessors), recent developments in GPU computing architectures, and how the enthusiastic adoption of CPU + GPU co-processing is accelerating parallel applications."

Read full article here:
Learn about MAGMA from the Experts
Professor Jack Dongarra and Dr. Stan Tomov of the University of Tennessee will present a webinar on "The MAGMA Project: Acceleration of Dense Linear Algebra on the GPU" on Monday, June 14 at 9:00 a.m. pacific. MAGMA (Matrix Algebra on GPU and Multicore Architectures) aims to develop a dense linear algebra library similar to LAPACK but for heterogeneous/hybrid architectures, starting with current "Multicore+GPU" systems.
– Prof. Jack Dongarra has a deep background in the development, testing and documentation
   of mathematical software. He has contributed to the design and implementation of open
   source software including EISPACK, LINPACK, the BLAS, LAPACK, ScaLAPACK,
   Netlib, PVM, MPI, NetSolve, Top500, ATLAS, and PAPI. See bio:
– Dr. Stan Tomov has been involved in research and development for LAPACK/scaLAPACK,
   multicore computing and MAGMA. Research interests include parallel algorithms and high
   performance scientific computing. See bio:
– Sign up for the MAGMA webinar here:
– This presentation is a feature of NVIDIA’s ongoing series of webinars on GPU Computing
Attention Photographers: New Photoshop Plugin Powered by CUDA
Digital Anarchy, provider of software for photographers and digital artists, recently introduced "Beauty Box Photo" skin retouching software - the world’s first CUDA-accelerated Photoshop plugin. The software automatically smoothes skin and removes blemishes, saving photographers hours of time. It identifies skin tones and creates an ‘intelligent mask’ to limit the smoothing effect to skin areas while keeping facial details sharp. Beauty Box Photo gets its render speed from new technology from Toonmation and leverages CUDA for up to 6X faster processing.
– Read about Beauty Box Photo:
– Read about Digital Anarchy:
– Read about Toonmation:
GPU-Accelerated Imaging and Vision Processing
In March we introduced you to Canada-based DALSA, a leader in machine vision systems. At a recent event in Boston called "The Vision Show," DALSA announced that GPUs enable the "Sapera Nitrous" product to accelerate image processing for filters, point-to-point, color and other image/vision processing functions. DALSA products are used across applications from solar panel inspection to postal automation.
– Read the press release:
– Learn more about Sapera Nitrous:
New on CUDA Zone: Acceleration of the Smith-Waterman Algorithm using GPUs
Application Domain: DNA/protein sequence testing
Authors: Ali Khajeh-Saeed, Univ. of Mass at Amherst; Stephen Poole, Oak Ridge National Lab; J. Blair Perot, Univ. of Mass at Amherst
Extract: "Finding regions of similarity between two very long data streams is a computationally intensive problem referred to as sequence alignment. Perhaps the most well known application of sequence matching is the testing of DNA or protein sequences against genome databases. The Smith-Waterman algorithm is a method for characterizing how well two sequences can be aligned…. The results indicate that for large problems a single GPU is up to 45 times faster than a CPU for this application, and the parallel implementation shows linear speed up on up to 4 GPUs." See:
CUDA Zone: Have a CUDA-related app or paper? Let us know when you post it on CUDA Zone and we'll send you a CUDA t-shirt!
KLA-Tencor, supplier of process control and yield management solutions, is seeking an algorithm engineer. The ideal candidate will have an M.S. or Ph.D. in Computer Science or Electrical Engineering, with a background in image processing or digital signal processing with an emphasis on computational algorithms, optimization and parallel computing. Experience using SIMD extensions or GPU programming (CUDA/OpenCL) is preferred. See:
GPU Computing Webinars (CUDA C, OpenCL, Parallel Nsight and more…)
See June webinar schedule: Upcoming webinars include:

– Intro to the MAGMA Project - Acceleration of Dense Linear Algebra
Monday, June 14, 2010, 9:00 a.m. pacific
Presented by Jack Dongarra and Stan Tomov, University of Tennessee

– Intro to CULA GPU Accelerated Linear Algebra
Tuesday, June 15, 2010, 9:00 a.m. pacific
Presented by EM Photonics, advanced computing solutions provider

– Rapid Application Development Platform for GPGPUs - Jacket / MATLAB
Tuesday, June 22, 2010, 8:00 a.m. pacific
Presented by AccelerEyes, developer of Jacket for MATLAB

– Intro to MainConcept's CUDA H.264/AVC Encoder
Tuesday, June 29, 2010, 9:00 a.m. pacific
Presented by MainConcept, video and audio codec solutions provider
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 training from EMPhotonics
On-site standard and customized training programs
CUDA Research and Certification
NVIDIA has launched new programs for GPU Computing developers. For more info, see:
CUDA and Academia
Over 350 universities are teaching CUDA and GPU Computing courses. We recently learned about a new course taught at the University of Jordan:

Course name: Parallel Computing
Location: University of Jordan in Amman, Jordan
Instructor: Dr. Walid Abu-Sufah
Lecture Notes:
CUDA Center of Excellence Program
The CUDA Center of Excellence (CCOE) Program recognizes universities that are expanding the frontier of massively parallel computing using CUDA. See:
DoD High Performance Computing Modernization Program (HPCMP) Users Group
June 14-17, Schaumburg, IL
Note: Stan Posey of NVIDIA will be a panelist on "Current and Future Applications Using GPUs, FPGAs and Cell Processors for HPC"

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

Virtual School of Comp. Science & Engineering Summer School
Aug. 2-6, choice of onsite locations (Proven Algorithmic Techniques for Many-Core Processors)

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:
Download the Parallel Nsight Beta Release Notes:
– NEW: Check out the NVIDIA Research site:
– 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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