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You searched for +publisher:"Universiteit Utrecht" +contributor:("Bisseling, R. H."). Showing records 1 – 3 of 3 total matches.

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1. Taviani, D. Iterative sparse matrix partitioning.

Degree: 2013, Universiteit Utrecht

At the core of many numerical methods lies one simple operation: the sparse matrix-vector multiplication. Because the systems involved are usually of large size, to speed up this computation we partition the matrix, dividing the work among different processors. This division requires communication between these processors, which has to be minimized. The goal of the thesis was to come up with different strategies to be used with the medium-grain method, to build an iterative framework that re-uses information on the current partitioning to lower the communication volume; furthermore, we tried to apply the same concepts to obtain a better initial partitioning of a matrix. Advisors/Committee Members: Bisseling, R. H..

Subjects/Keywords: matrix; partitioning; sparse matrix; hypergraph; heuristics

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APA (6th Edition):

Taviani, D. (2013). Iterative sparse matrix partitioning. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/285045

Chicago Manual of Style (16th Edition):

Taviani, D. “Iterative sparse matrix partitioning.” 2013. Masters Thesis, Universiteit Utrecht. Accessed October 15, 2019. http://dspace.library.uu.nl:8080/handle/1874/285045.

MLA Handbook (7th Edition):

Taviani, D. “Iterative sparse matrix partitioning.” 2013. Web. 15 Oct 2019.

Vancouver:

Taviani D. Iterative sparse matrix partitioning. [Internet] [Masters thesis]. Universiteit Utrecht; 2013. [cited 2019 Oct 15]. Available from: http://dspace.library.uu.nl:8080/handle/1874/285045.

Council of Science Editors:

Taviani D. Iterative sparse matrix partitioning. [Masters Thesis]. Universiteit Utrecht; 2013. Available from: http://dspace.library.uu.nl:8080/handle/1874/285045


Universiteit Utrecht

2. Eelaart, X. van den. Matching and Scheduling Algorithms on Large Scale Dynamic Graphs.

Degree: 2014, Universiteit Utrecht

The research for this thesis was conducted at Progressive Planning, an online multi-project management tool. For Progressive Planning, the precedence graph determines a partial ordering of the execution of jobs in each project. This graph is large, and data changes frequently. Edges, vertices and weights can be added, removed or updated. In this setting, I study heuristic algorithms both for scheduling tasks of projects in Progressive Planning, and for the maximum weighted matching problem. I developed an algorithm to quickly schedule a large number of jobs, using a simple, but effective decision measure. Furthermore, I reduce the problem size by dynamically maintaining connected components. For the maximum weighted matching problem, I developed a simple algorithm that maintains a half-approximation on incremental, linear graphs. This algorithm can also maintain a matching of unverified quality on generic incremental graphs. Lastly, I show that this algorithm is well suited for parallel computing. Advisors/Committee Members: Bisseling, R. H..

Subjects/Keywords: matching; scheduling; graphs; scientific; computing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Eelaart, X. v. d. (2014). Matching and Scheduling Algorithms on Large Scale Dynamic Graphs. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/297709

Chicago Manual of Style (16th Edition):

Eelaart, X van den. “Matching and Scheduling Algorithms on Large Scale Dynamic Graphs.” 2014. Masters Thesis, Universiteit Utrecht. Accessed October 15, 2019. http://dspace.library.uu.nl:8080/handle/1874/297709.

MLA Handbook (7th Edition):

Eelaart, X van den. “Matching and Scheduling Algorithms on Large Scale Dynamic Graphs.” 2014. Web. 15 Oct 2019.

Vancouver:

Eelaart Xvd. Matching and Scheduling Algorithms on Large Scale Dynamic Graphs. [Internet] [Masters thesis]. Universiteit Utrecht; 2014. [cited 2019 Oct 15]. Available from: http://dspace.library.uu.nl:8080/handle/1874/297709.

Council of Science Editors:

Eelaart Xvd. Matching and Scheduling Algorithms on Large Scale Dynamic Graphs. [Masters Thesis]. Universiteit Utrecht; 2014. Available from: http://dspace.library.uu.nl:8080/handle/1874/297709


Universiteit Utrecht

3. Kurt, H. Improving the Mondriaan vector distribution.

Degree: 2016, Universiteit Utrecht

Mondriaan is a hypergraph based matrix partitioner, used to distribute the matrix and vectors in parallel sparse matrix-vector multiplication (SpMV) when calculating the product u=Av. In this study, we investigate the problem of distributing the input vector v over our P processors, in order to reduce the number of messages, while keeping the communication volume more or less equal. A novel method assigning each vector element to the lowest numbered processor gives us significantly lower total message count, while keeping the communication volume constant. Another method, a novel hypergraph based heuristic, roughly halves the total amount of messages, while it increases the communication volume. Both newly developed methods provide large reductions in the total amount of messages and can hence be considered as alternative vector distribution methods. Advisors/Committee Members: Bisseling, R. H..

Subjects/Keywords: parallel algorithms; mondriaan; vector distribution

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kurt, H. (2016). Improving the Mondriaan vector distribution. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/327907

Chicago Manual of Style (16th Edition):

Kurt, H. “Improving the Mondriaan vector distribution.” 2016. Masters Thesis, Universiteit Utrecht. Accessed October 15, 2019. http://dspace.library.uu.nl:8080/handle/1874/327907.

MLA Handbook (7th Edition):

Kurt, H. “Improving the Mondriaan vector distribution.” 2016. Web. 15 Oct 2019.

Vancouver:

Kurt H. Improving the Mondriaan vector distribution. [Internet] [Masters thesis]. Universiteit Utrecht; 2016. [cited 2019 Oct 15]. Available from: http://dspace.library.uu.nl:8080/handle/1874/327907.

Council of Science Editors:

Kurt H. Improving the Mondriaan vector distribution. [Masters Thesis]. Universiteit Utrecht; 2016. Available from: http://dspace.library.uu.nl:8080/handle/1874/327907

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