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» Performance Tuning of Iterative Algorithms in Signal Process...
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2009
ACM
15 years 4 months ago
Autotuning multigrid with PetaBricks
Algorithmic choice is essential in any problem domain to realizing optimal computational performance. Multigrid is a prime example: not only is it possible to make choices at the ...
Cy P. Chan, Jason Ansel, Yee Lok Wong, Saman P. Am...
CHI
2010
ACM
15 years 3 months ago
Exploring iterative and parallel human computation processes
Services like Amazon’s Mechanical Turk have opened the door for exploration of processes that outsource computation to humans. These human computation processes hold tremendous ...
Greg Little
MDM
2005
Springer
165views Communications» more  MDM 2005»
15 years 3 months ago
STEP: Self-Tuning Energy-safe Predictors
Data access prediction has been proposed as a mechanism to overcome latency lag, and more recently as a means of conserving energy in mobile systems. We present a fully adaptive p...
James Larkby-Lahet, Ganesh Santhanakrishnan, Ahmed...
ICASSP
2008
IEEE
15 years 4 months ago
Wavelet-domain compressive signal reconstruction using a Hidden Markov Tree model
Compressive sensing aims to recover a sparse or compressible signal from a small set of projections onto random vectors; conventional solutions involve linear programming or greed...
Marco F. Duarte, Michael B. Wakin, Richard G. Bara...
CISS
2008
IEEE
15 years 4 months ago
Distributed processing in frames for sparse approximation
—Beyond signal processing applications, frames are also powerful tools for modeling the sensing and information processing of many biological and man-made systems that exhibit in...
Christopher J. Rozell