[Noisebridge-discuss] UCB talk: Accelerating Computer Vision and Machine Learning Algorithms with Graphics Processors

[Kelly]

The Redwood Institute are the badasses of neuroscience around here.
This talk seemed accessible to non-neuro people as well, so I thought
I'd forward it on, but really all of their talks are pretty cool.

-Kelly


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Date: Tue, Jan 19, 2010 at 12:21 PM
Subject: redwood Digest, Vol 26, Issue 3
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Today's Topics:

  1. Redwood Seminar - Tom Dean (Bruno Olshausen)


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Message: 1
Date: Tue, 19 Jan 2010 01:01:52 -0800
From: Bruno Olshausen <baolshausen at berkeley.edu>
Subject: [redwood] Redwood Seminar - Tom Dean
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       vs-faculty at lists.berkeley.edu, vs-students at lists.berkeley.edu,
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Redwood Seminar:
---------------------------


?Accelerating Computer Vision and Machine Learning Algorithms with
Graphics Processors?

Tom Dean, Google


Wednesday, January 20, at 12:00
508?20 Evans Hall


Graphics processors (GPUs) and massively-multi-core architectures are
becoming more powerful, less costly and more energy efficient, and the
related programming language issues are beginning to sort themselves
out. That said most researchers don?t want to be writing code that
depends on any particular architecture or parallel programming model.
Linear algebra, Fourier analysis and image processing have standard
libraries that are being ported to exploit SIMD parallelism in GPUs.
We can depend on the massively-multiple-core machines du jour to
support these libraries and on the high-performance-computing (HPC)
community to do the porting for us or with us. These libraries can
significantly accelerate important applications in image processing,
data analysis and information retrieval. We can develop APIs and the
necessary run-time support so that code relying on these libraries
will run on any machine in a cluster of computers but exploit GPUs
whenever available. This strategy allows us to move toward hybrid
computing models that enable a wider range of opportunities for
parallelism without requiring the special training of programmers or
the disadvantages of developing code that depends on specialized
hardware or programming models. This talk summarizes the state of the
art in massively-multi-core architectures, presents experimental
results that demonstrate the potential for significant performance
gains in the two general areas of image processing and machine
learning, provides examples of the proposed programming interface, and
some more detailed experimental results on one particular problem
involving video-content analysis.


---------------------------------------
Bruno A. Olshausen
Helen Wills Neuroscience Institute & School of Optometry,
and Redwood Center for Theoretical Neuroscience, UC Berkeley
Mail address:   156 Stanley Hall MC 3220
Berkeley, CA 94720-3220
(510) 642-7250 / 2-7206 (fax)
http://redwood.berkeley.edu/bruno









End of redwood Digest, Vol 26, Issue 3
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