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478
Scalespace and edge detection using anisotropic diffusion
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1990
"... AbstractThe scalespace technique introduced by Witkin involves generating coarser resolution images by convolving the original image with a Gaussian kernel. This approach has a major drawback: it is difficult to obtain accurately the locations of the “semantically meaningful ” edges at coarse sca ..."
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Cited by 1887 (1 self)
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. The algorithm involves elementary, local operations replicated over the image making parallel hardware implementations feasible. Index TermsAdaptive filtering, analog VLSI, edge detection, edge enhancement, nonlinear diffusion, nonlinear filtering, parallel algo
SPRINT: A scalable parallel classifier for data mining
, 1996
"... Classification is an important data mining problem. Although classification is a wellstudied problem, most of the current classification algorithms require that all or a portion of the the entire dataset remain permanently in memory. This limits their suitability for mining over large databases. ..."
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Cited by 312 (8 self)
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. We present a new decisiontreebased classification algorithm, called SPRINT that removes all of the memory restrictions, and is fast and scalable. The algorithm has also been designed to be easily parallelized, allowing many processors to work together to build a single consistent model
AlgoWiki: an Open Encyclopedia of Parallel Algorithmic Features
"... c © The Authors 2015. This paper is published with open access at SuperFri.org The main goal of this project is to formalize the mapping of algorithms onto the architecture of parallel computing systems. The basic idea is that features of algorithms are independent of any computing system. A detaile ..."
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of algorithms implementation for different parallel architectures. The proposed description of algorithms includes many nontrivial features such as: parallel algorithm complexity, resource of parallelism and its properties, features of the informational graph, computational cost of algorithms, data locality
Exploring network structure, dynamics, and function using NetworkX
 In Proceedings of the 7th Python in Science Conference (SciPy
, 2008
"... NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and selfloops. The nodes in ..."
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Cited by 180 (0 self)
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NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and selfloops. The nodes
Parallel Algorithms for GPU accelerated Probabilistic Inference
"... Real world data is likely to contain an inherent structure. Those structures may be represented with graphs which encode independence assumptions within the data. Performing inference in those models is nearly intractable on mobile devices or casual workstations. This work introduces and compares tw ..."
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Cited by 1 (1 self)
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two approaches for accelerating the inference in graphical models by using GPUs as parallel processing units. It is empirically showed, that in order to achieve a scaleable parallel algorithm, one has to distribute the workload equally among all processing units of a GPU. We accomplished
An Optimal Parallel Algorithm for Minimum Spanning Trees in Planar Graphs
"... Abstract. We present an optimal deterministic O(n)work parallel algorithm for finding a minimum spanning tree on an nvertex planar graph. The algorithm runs in O(log n) time on a CRCW PRAM and in O(log n log ∗ n) time on an EREW PRAM. Our results hold for any sparse graph that is closed under tak ..."
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Abstract. We present an optimal deterministic O(n)work parallel algorithm for finding a minimum spanning tree on an nvertex planar graph. The algorithm runs in O(log n) time on a CRCW PRAM and in O(log n log ∗ n) time on an EREW PRAM. Our results hold for any sparse graph that is closed under
OpenVIDIA: Parallel GPU computer vision
 In ACM Multimedia
, 2005
"... Graphics and vision are approximate inverses of each other: ordinarily Graphics Processing Units (GPUs) are used to convert \numbers into pictures " (i.e. computer graphics). In this paper, we propose using GPUs in approximately the reverse way: to assist in \converting pictures into numbers&qu ..."
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Cited by 76 (1 self)
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plores the creation of a parallel computer architecture consisting of multiple Graphics Processing Units (GPUs) built entirely from commodity hardware. OpenVIDIA uses multiple Graphics Processing Units in parallel to operate as a generalpurpose parallel computer architecture. It provides a simple API which
Fast Parallel Solution of Boundary Integral Equations and Related Problems
, 2004
"... This article is concerned with the efficient numerical solution of Fredholm integral equations on a parallel computer with shared or distributed memory. Parallel algorithms for both, the approximation of the discrete operator by hierarchical matrices and the parallel matrixvector multiplication of ..."
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Cited by 3 (1 self)
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This article is concerned with the efficient numerical solution of Fredholm integral equations on a parallel computer with shared or distributed memory. Parallel algorithms for both, the approximation of the discrete operator by hierarchical matrices and the parallel matrixvector multiplication
PFAS: A ResourcePerformanceFluctuationAware Workflow Scheduling Algo rithm for Grid Computing
 21st Parallel and Distributed Processing Symposium (IPDPS'07
, 2007
"... Resource performance in the Computational Grid is not only heterogeneous, but also changing dynamically. However scheduling algorithms designed for traditional parallel and distributed systems, such as clusters, only consider the heterogeneity of the resources. In this paper, a workflow scheduling a ..."
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Cited by 4 (0 self)
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Resource performance in the Computational Grid is not only heterogeneous, but also changing dynamically. However scheduling algorithms designed for traditional parallel and distributed systems, such as clusters, only consider the heterogeneity of the resources. In this paper, a workflow scheduling
Parallel algorithms for the solution of toeplitz systems of linear equations
 Lecture Notes in Computer Science
"... Abstract. In this paper we present two parallel algorithms to solve nonsymmetric Toeplitz systems of linear equations. The first algorithm performs a modified QR factorization of the matrix by using the generalized Schur algorithm. The second one is based on the transformation of the Toeplitz matr ..."
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Cited by 3 (3 self)
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matrix into a Cauchylike matrix in order to reduce the communication cost. Both sequential methods have small computational cost. This fact makes it difficult to implement efficient parallel algorithms. We have tested the efficiency and stability of the algorithms on a cluster of personal computers
Results 1  10
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