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» Accelerating Large Data Analysis by Exploiting Regularities
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Publication
248views
14 years 9 months ago
Equalizer: A Scalable Parallel Rendering Framework
Continuing improvements in CPU and GPU performances as well as increasing multi-core processor and cluster-based parallelism demand for flexible and scalable parallel rendering sol...
Stefan Eilemann, Maxim Makhinya, Renato Pajarola
KDD
2012
ACM
271views Data Mining» more  KDD 2012»
13 years 2 months ago
GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries
Many data are modeled as tensors, or multi dimensional arrays. Examples include the predicates (subject, verb, object) in knowledge bases, hyperlinks and anchor texts in the Web g...
U. Kang, Evangelos E. Papalexakis, Abhay Harpale, ...
SDM
2012
SIAM
237views Data Mining» more  SDM 2012»
13 years 2 months ago
A Distributed Kernel Summation Framework for General-Dimension Machine Learning
Kernel summations are a ubiquitous key computational bottleneck in many data analysis methods. In this paper, we attempt to marry, for the first time, the best relevant technique...
Dongryeol Lee, Richard W. Vuduc, Alexander G. Gray
ICCV
2011
IEEE
13 years 11 months ago
3D Scene Flow Estimation with a Rigid Motion Prior
We present an approach to 3D scene flow estimation, which exploits that in realistic scenarios image motion is frequently dominated by observer motion and independent, but rigid ...
Christoph Vogel, Stefan Roth, Konrad Schindler
PLDI
2011
ACM
14 years 2 months ago
The tao of parallelism in algorithms
For more than thirty years, the parallel programming community has used the dependence graph as the main abstraction for reasoning about and exploiting parallelism in “regular...
Keshav Pingali, Donald Nguyen, Milind Kulkarni, Ma...