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11-10-11, 05:40 PM
AccelerEyes and NVIDIA invite you to participate in series of webinars (https://accelereyes.webex.com/mw0306ld/mywebex/default.do?nomenu=true&siteurl=accelereyes&service=6&rnd=0.4385461764274333&main_url=https%3A%2F%2Faccelereyes.webex.com%2Fec0 605ld%2Feventcenter%2Fprogram%2FprogramDetail.do%3 FtheAction%3Ddetail%26siteurl%3Daccelereyes%26cPro gViewID%3D0) designed to help you learn more about Jacket for MATLAB and LibJacket for C/C++/Fortran/Python.

Jacket accelerates MATLAB (a numerical computing environment) code on GPUs. LibJacket is a software library which accelerates C/C++ code on GPUs. It provides the world‚??s largest collection of GPU-accelerated functions.¬*These functions include fast prepacked convolutions, reductions, matrix indexing, linear algebra, image processing, signal processing, statistics functions, and much more.

LibJacket CUDA Library: Recorded webinar (http://developer.download.nvidia.com/CUDA/training/LibJacket_Oct2011.mp4)

http://blogs.nvidia.com/wp-content/uploads/2011/11/jacket-webinar-image-1.jpg (http://blogs.nvidia.com/wp-content/uploads/2011/11/jacket-webinar-image-1.jpg)Graphics library: Tweaked FDTD
(example in LibJacket package).

LibJacket‚??s ability to leverage hundreds of GPU computing functions (http://wiki.accelereyes.com/wiki/libjacket/modules.htm)¬*allows programmers in numerous industries to realize significant speedups. In case you missed it, we co-hosted a joint webinar with NVIDIA (http://developer.download.nvidia.com/CUDA/training/LibJacket_Oct2011.mp4) on October 20th. During this event, our GPU computing experts provided a general product overview of the LibJacket CUDA library. We also provided several demos of LibJacket in action. Be sure to check out the recorded webinar (http://developer.download.nvidia.com/CUDA/training/LibJacket_Oct2011.mp4) posted on NVIDIA‚??s Developer Zone, including the Q&A session.

GPU Programming for Defense/Intelligence Apps: November 15, 2011 (https://accelereyes.webex.com/mw0306ld/mywebex/default.do?nomenu=true&siteurl=accelereyes&service=6&rnd=0.5380165052672361&main_url=https%3A%2F%2Faccelereyes.webex.com%2Fec0 605ld%2Feventcenter%2Fprogram%2FprogramDetail.do%3 FtheAction%3Ddetail%26siteurl%3Daccelereyes%26cPro gViewID%3D0) at 3:00 p.m. EST

Major defense and intelligence institutions are discovering how GPUs can be used with Jacket and LibJacket to enhance and speed up computationally intensive applications related to video analysis, recognition, tracking, etc. This webinar will explain how to accelerate common defense and intelligence algorithms using easy, powerful programming libraries in Jacket for MATLAB and LibJacket for C/C++/Fortran.

Register for this webinar by clicking here (http://accelereyes.webex.com/mw0306ld/mywebex/default.do?siteurl=accelereyes).

LibJacket CUDA Library for Maximus Applications:¬* December 15, 2011 (https://accelereyes.webex.com/mw0306ld/mywebex/default.do?nomenu=true&siteurl=accelereyes&service=6&rnd=0.5380165052672361&main_url=https%3A%2F%2Faccelereyes.webex.com%2Fec0 605ld%2Feventcenter%2Fprogram%2FprogramDetail.do%3 FtheAction%3Ddetail%26siteurl%3Daccelereyes%26cPro gViewID%3D0) at 3:00 p.m. EST

http://blogs.nvidia.com/wp-content/uploads/2011/11/jacket-webinar-image-2.jpg (http://blogs.nvidia.com/wp-content/uploads/2011/11/jacket-webinar-image-2.jpg)Graphics library: simulating shallow water
equations with reflective¬*boundary conditions.
This webinar discusses methods to integrate computations with visualizations in a CUDA-based application, using simple visualization functions for plotting, image and volume rendering, and more.

This webinar series will be conducted by AccelerEyes‚?? team of GPU computing experts and will include live demos of Jacket and LibJacket. We hope you will join us (http://accelereyes.webex.com/mw0306ld/mywebex/default.do?siteurl=accelereyes) as we discuss exciting developments in GPU computing software!

Register for this webinar by clicking here (http://accelereyes.webex.com/mw0306ld/mywebex/default.do?siteurl=accelereyes).

http://feeds.feedburner.com/~ff/nvidiablog?d=yIl2AUoC8zA (http://feeds.feedburner.com/~ff/nvidiablog?a=A7_DMv2k14c:6jHHZqvrT1A:yIl2AUoC8zA) http://feeds.feedburner.com/~ff/nvidiablog?i=A7_DMv2k14c:6jHHZqvrT1A:V_sGLiPBpWU (http://feeds.feedburner.com/~ff/nvidiablog?a=A7_DMv2k14c:6jHHZqvrT1A:V_sGLiPBpWU)
http://feeds.feedburner.com/~r/nvidiablog/~4/A7_DMv2k14c

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