The paper covers the mathematical modelling of the main biogenic matter transformations in the production-destruction processes of phytoplankton populations in the Azov Sea, taking into account the influence of extern...
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In the analysis of methods of multicriteria optimization. The detailed implementation of the parallel algorithm of the simulated annealing method is reproduced by the example of the extension of a large-scale travelli...
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Is matching in NC, i.e., is there a deterministic fast parallel algorithm for it? This has been an outstanding open question in TCS for over three decades, ever since the discovery of randomized NC matching algorithms...
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The massive size and complexity of big datasets such as those coming from social, natural and sensor environments raise utmost challenges to unsupervised cluster analysis methods in terms of performance scalability in...
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Gamma oscillations have been not only found in many biology experiments but also regenerated in many small neural network models. However, whether gamma oscillations can be regenerated in large-scale neural network wi...
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The Reeb graph of a scalar function that is defined on a domain gives a topologically meaningful summary of that domain. Reeb graphs have been shown in the past decade to be of great importance in geometric processing...
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The Reeb graph of a scalar function that is defined on a domain gives a topologically meaningful summary of that domain. Reeb graphs have been shown in the past decade to be of great importance in geometric processing, image processing, computer graphics, and computational topology. The demand for analyzing large data sets has increased in the last decade. Hence, the parallelization of topological computations needs to be more fully considered. We propose a parallel augmented Reeb graph algorithm on triangulated meshes with and without a boundary. That is, in addition to our parallel algorithm for computing a Reeb graph, we describe a method for extracting the original manifold data from the Reeb graph structure. We demonstrate the running time of our algorithm on standard datasets. As an application, we show how our algorithm can be utilized in mesh segmentation algorithms.
In this paper, we study scalable parallel algorithms for estimating the farness-centrality value of the nodes in a given undirected and connected graph. Our algorithms consider approaches that are more suitable for sp...
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ISBN:
(纸本)9781538655559
In this paper, we study scalable parallel algorithms for estimating the farness-centrality value of the nodes in a given undirected and connected graph. Our algorithms consider approaches that are more suitable for sparse graphs. To this end, we propose four optimization techniques based on removing redundant nodes, removing identical nodes, removing chain nodes, and making use of decomposition based on the biconnected components of the input graph. We test our techniques on a collection of real-world graphs for the time taken and the average error percentage. We further analyze the applicability of our techniques on various classes of real-world graphs. We suggest why certain techniques work better on certain classes of graphs.
In this paper, we present an efficient parallel algorithm for computing the visibility region for a point in a plane among a non-intersecting set of segments. The algorithm is based on the cascading divide-and-conquer...
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In this paper, we present an efficient parallel algorithm for computing the visibility region for a point in a plane among a non-intersecting set of segments. The algorithm is based on the cascading divide-and-conquer technique and uses merge path to evenly distribute the workload between processors. We implemented the algorithm on NVIDIA's CUDA platform where it performed with a speedup up to 76x with respect to the serial CPU version.
In this paper we describe a general framework for parallel optimization based on the island model of evolutionary algorithms. The framework runs a number of optimization methods in parallel with periodic communication...
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Markovian Arrival Processes (MAPs) are widely used stochastic models to describe correlated events. For the parameter fitting of MAPs according to measured data, the expectation-maximization (EM) algorithm is commonly...
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