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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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Department: search.filters.department.Department of Computer EngineeringSubject: search.filters.subject.Applied mathematics

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Now showing 1 - 3 of 3
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    Data decomposition for parallel K-means clustering
    (Springer-Verlag Berlin, 2004) Department of Computer Engineering; Gürsoy, Attila; Faculty Member; Department of Computer Engineering; College of Engineering; 8745
    Developing fast algorithms for clustering has been an important area of research in data mining and other fields. K-means is one of the widely used clustering algorithms. In this work, we have developed and evaluated parallelization of k-means method for low-dimensional data on message passing computers. Three different data decomposition schemes and their impact on the pruning of distance calculations in tree-based k-means algorithm have been studied. Random pattern decomposition has good load balancing but fails to prune distance calculations effectively. Compact spatial decomposition of patterns based on space filling curves outperforms random pattern decomposition even though it has load imbalance problem. In both cases, parallel tree-based k-means clustering runs significantly faster than the traditional parallel k-means.
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    Boxlib with tiling: an adaptive mesh refinement software framework
    (Siam Publications, 2016) Zhang, Weiqun; Almgren, Ann; Day, Marcus; Tan Nguyen; Shalf, John; Department of Computer Engineering; Erten, Didem Unat; Faculty Member; Department of Computer Engineering; College of Engineering; 219274
    In this paper we introduce a block-structured adaptive mesh refinement software framework that incorporates tiling, a well-known loop transformation. Because the multiscale, multiphysics codes built in BoxLib are designed to solve complex systems at high resolution, performance on current and next generation architectures is essential. With the expectation of many more cores per node on next generation architectures, the ability to effectively utilize threads within a node is essential, and the current model for parallelization will not be sufficient. We describe a new version of BoxLib in which the tiling constructs are embedded so that BoxLib-based applications can easily realize expected performance gains without extra effort on the part of the application developer. We also discuss a path forward to enable future versions of BoxLib to take advantage of NUMA-aware optimizations using the TiDA portable library.
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    Context-sensitive mental model aggregation in a second-order adaptive network model for organisational learning
    (Springer International Publishing AG, 2022) Treur, Jan; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/A
    Organisational learning processes often exploit developed individual mental models in order to obtain shared mental models for the organisation by some form of unification or aggregation. The focus in this paper is on this aggregation process, which may depend on a number of contextual factors. It is shown how a second-order adaptive network model for organisation learning can be used to model this process of aggregation of individual mental models in a context-dependent manner.