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Using these geometric observations, we obtain optimal sequential and parallel algorithms for solving this problem. ... In this manuscript we present efficient sequential and parallel algorithms for the SRQ problem when the barriers in & are disjoint planar rectangles whose sides are parallel to the coordinate axes, and ...
This paper investigates the design of parallel algorithmic strategies that address the efficient use of both, memory hierarchies within each processor and a multilevel clustered structure of the interconnection ... times for a number of geometry problems. ... For some of the classical geometric problems mentioned above, parallel algorithms were pre-sented in  which run on a particular parallel architecture consisting of a d-dimensional array of p processors ...doi:10.1109/ipdps.2002.1015508 dblp:conf/ipps/DehneMPP02 fatcat:2czwcou3bzdy5orgdhomhegepa
Lecture Notes in Computer Science
By leveraging the inherent parallelism of the problem and through the use of efficient GPU-based algorithms, our system is able to effectively summarize datasets containing up to three million images in ... A subset of the iconic images automatically found by our system, for the Berlin dataset. ... Algorithm 1 K-Medoids for i=1 to k do randomly assign medoid[i] to a binary code end for repeat for i=1 to n do compute distance of ith binary code to medoids in parallel do parallel min-reduce to assign ...doi:10.1007/978-3-642-35740-4_36 fatcat:pfan6bhkirhf3ccpoq6kh6v47q
In this study, we propose a parallel and distributed k-means clustering algorithm with naïve sharding centroid initialization for image segmentation. ... In digital image processing, image segmentation is an essential step in which an image is partitioned into groups of pixels. k-means clustering algorithm, which is often considered as fast and efficient ... ., 2011) , they presented a parallelized k-means algorithm, MKmeans, which focuses on how to efficiently handle huge volumes of data by using MPI. ...doi:10.21923/jesd.748209 fatcat:2s2ehc3fcbbdbnv4k2tpcbqrmy
The work presents a way to execute optimization calculations with the genetic algorithm method on a parallel computer of the cluster type. ... Sample indicators describing the quality of paralleling the calculation process were provided. Streszczenie. ... The efficiency of the implementation of the optimization problem analyzed with the use of a genetic algorithm on a computer cluster was examined. ...doi:10.15199/48.2015.07.12 fatcat:75ybwkdjn5gz3llou45no44dq4
It covers both traditional approaches originally designed for clusters of heterogeneous workstations and the most recent methods developed in the context of modern multicore and multi-accelerator heterogeneous ... The paper overviews the state of the art in design and implementation of data parallel scientific applications on heterogeneous platforms. ... More restricted forms of the column-based geometrical partitioning problem have also Heterogeneous Parallel Computing: from Clusters of Workstations to Hierarchical... been addressed. ...doi:10.14529/jsfi140304 fatcat:f4u6c4zupjhvlg5c666shbxto4
2014 International Conference on High Performance Computing and Applications (ICHPCA)
This research work analyzes about the performance of parallel k means algorithm and based on this algorithm ,we propose a new parallel architecture combined with PKM,FCM and FA algorithm. ... Firefly-based clustering is a recent method which proves better for optimal clustering finding. ... We use two dataset for calculating CEP and efficiency for the new algorithm. ...doi:10.1109/ichpca.2014.7045322 fatcat:hq2rz5iy45ethm7c7wbmzdhmlm
This new approach is based on the matrix graph of the sparse stiffness matrix and no longer requires geometric data associated with the indices like the standard clustering algorithms. ... In the context of finite element discretisations of elliptic boundary value problems, H -matrices can be used for the construction of preconditioners such as approximate H -LU factors. ... As for the Poisson problem, nested dissection significantly improves the computational efficiency and reduces the memory consumption. ...doi:10.1007/s00791-008-0098-9 fatcat:hnq7xkjq3belvotpbcrk2s3ici
Ádám isomor- phism of circulant graphs, 324 Circular graph drawing A framework and algorithms for circular drawings of graphs, 25 Clustered graphs Completely connected clustered graphs, 313 Clustered ... assignment Radiocolorings in periodic planar graphs: PSPACE- completeness and efficient approximations for the optimal range of frequencies, 433 Fuzzy ART Art networks with geometrical distances, ...doi:10.1016/s1570-8667(06)00094-3 fatcat:e5in7ez4svbdlcjyzzev4xkzhm
Lecture Notes in Computer Science
While Single-Objective Evolutionary Algorithms (EAs) parallelization schemes are both well established and easy to implement, this is not the case for Multi-Objective Evolutionary Algorithms (MOEAs). ... We also suggest a clustering based parallelization scheme for MOEAs and compare it to several alternative MOEA parallelization schemes on multiple standard multi-objective test functions. ... For example, in case that a clustering algorithm identifies a single cluster in a contiguous problem space, the parallelization scheme could proceed with a simple island MOEA with migration. ...doi:10.1007/978-3-540-31880-4_7 fatcat:jh3a7xydunaibjbkcahqh2ww5q
Zhang, A new efficient parallel algorithm for computing eigenvalues of symmet- ric tridiagonal matrices (14 pp.); Dror Irony and Sivan Toledo, Communication-efficient parallel dense LU using a 3-dimensional ... Lamont, Load balancing search algorithms on a het- erogeneous cluster of PCs (10 pp.); Shannon K. Kuntz, Richard C. Murphy, Michael T. Niemier, Jesus Izaguirre and Peter M. ...
We will propose in this paper an intelligent partitioning approach using a cluster based algorithm combined with the representative Skyline. ... Most of the solutions dealing with large scale systems propose a parallel Skyline phase performed on a partitioned data space to preselect the best web services candidates. ... In this paper, we choose not to use the geometrical approach and to base our work on K-Means algorithm. ...doi:10.5220/0006904106820689 dblp:conf/icsoft/RegaiegYA18 fatcat:5l3iqqlh4ve3zb3p7iqyn3ga34
We describe a technique for parallelizing a family of center-based data clustering algorithms. ... To cluster such large and distributed data sets, efficient distributed algorithms are called for to reduce the communication overhead, central storage requirements, and computation time, as well as to ... Larry Snyder, Douglas Low and the other students of the ZPL Parallel Language and Optimizing Compiler research project at the University of Washington for ZPL and their support. ...doi:10.1145/380995.381010 fatcat:7gnncjypjndvjk6nmxosh3hovq
We show that a simple algorithm for computing a matching on a graph runs in a logarithmic number of phases incurring work linear in the input size. ... The algorithm can be adapted to provide efficient algorithms in several models of computation, such as PRAM, External Memory, MapReduce and distributed memory models. ... Our experiments indicate that the algorithm yields surprisingly good quality for the weighted matching problem and runs very efficiently on sequential machines, clusters with reasonably partitioned input ...arXiv:1302.4587v2 fatcat:nrz5lugzxnhm5ltoraoimlh644
We present an efficient and provably good partitioning and load balancing algorithm for parallel adaptive N-body simulation. ... We then use our partitioning algorithm in the design of an efficient parallel divide-andconquer algorithm for constructing the N-body communication graph. ... I would like to express my appreciation to the 1995 SIAM Conference on Parallel Processing for Scientific Computing for bringing together experts on N-body simulation. ...doi:10.1137/s1064827595288942 fatcat:owpoxuut7vaqpbugoqufpqo4qm
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