Media
Media organizations, internet companies, and technology development enterprises are finding more and more needs for technical computing in research and development departments. The pervasiveness of GPUs across enterprises and the need for better, faster, and cheaper ways of performing modeling, simulation, and analytics make ArrayFire and Jacket perfect solutions for industries looking for high performance applications.
Companies such as Google and Adobe have found that Jacket provides a unique way to leverage the power and flexibility of GPU computing without the heavy investment in development time and resources.
Example Applications
ArrayFire and Jacket support programming in many application areas including:
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Video Processing |
Speedup: 10X - 20X |
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Video Processing
Authors: Google and Stanford University Video content analysis is the basis for categorizing videos and enabling search by content. Growing interest in using sparse-coding methods to extract motion features in video in support of video content analysis led to the application of Jacket and GPUs to improve performance by substantially accelerating the solution of the L1-regularized least-squares optimization problem. Last Updated: 13 Jan 2010 |
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Action Recognition with Independent Subspace Analysis Stanford University |
Speedup: 4.4X |
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Action Recognition with Independent Subspace Analysis
Authors: Quoc Le, Will Zou, Serena Yeung, Andrew Ng, Stanford University In a paper at this year's CVPR 2011, entitled "Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis", the authors explain how their unsupervised feature learning algorithm competes with other algorithms that are hand crafted or use learned features. For their training purposes, they used a multi-layered stacked convolutional ISA (Independent subspace analysis) network. An ISA is used for learning features from image patches without supervision. Last Updated: 19 Aug 2011 |
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Music Beat Analysis Georgia Tech |
Speedup: 15X |
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Music Beat Analysis
Authors: Vidhur Vohra - Georgia Tech Did you ever wonder how the music visualizer in your media player works? Watching it pulsate in synchrony with the beats of the song is almost as entertaining as listening to the song itself! Researchers have been attempting to detect beats in audio signals for many years, and there are many techniques available, from the simplest (and least accurate) to more complicated algorithms that are highly accurate. All algorithms, though, perform some form of signal processing and frequency analysis, applications highly suited to GPU Computing. Last Updated: 11 Aug 2011 |
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Feature Learning on Images Stanford University |
Speedup: Hours of runtime reduction |
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Feature Learning Architectures with GPU-acceleration
Authors: Andrew Ng, Stanford University Stanford researchers in Andrew Ng’s group used GPUs and Jacket to speed up their work on Feature Learning Architectures. They decided to use GPUs and Jacket for this study because of the need to quickly evaluate many architectures on thousands of images. Jacket taps into the immense computing power of GPUs and speeds up research utilizing many images. Last Updated: 9 Apr 2011 |
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Digital Holography for Imaging National University of Ireland, Maynooth |
Speedup: 17X |
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Digital Holography
Authors: Nitesh Pandey, Damien Kelly, Bryan Hennelly and
Thomas Naughton from the National University of Ireland,
Maynooth
Digital holography is a powerful imaging technique with many new
applications like true 3D display. It allows the capture of both
amplitude and phase information of the light reflected off the
surface of 3D objects. Researchers at the National University of
Ireland, Maynooth are developing techniques based on digital
holography for 3D display applications. |