Displaying similar documents to “Parallelization algorithms for modeling ARM processes.”

Event-Based Proof of the Mutual Exclusion Property of Peterson’s Algorithm

Ievgen Ivanov, Mykola Nikitchenko, Uri Abraham (2015)

Formalized Mathematics

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Proving properties of distributed algorithms is still a highly challenging problem and various approaches that have been proposed to tackle it [1] can be roughly divided into state-based and event-based proofs. Informally speaking, state-based approaches define the behavior of a distributed algorithm as a set of sequences of memory states during its executions, while event-based approaches treat the behaviors by means of events which are produced by the executions of an algorithm. Of...

EasyMSG : tools and techniques for an adaptive overlapping in SPMD programming

Pascal Havé (2002)

ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique

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During the development of a parallel solver for Maxwell equations by integral formulations and Fast Multipole Method (FMM), we needed to optimize a critical part including a lot of communications and computations. Generally, many parallel programs need to communicate, but choosing explicitly the way and the instant may decrease the efficiency of the overall program. So, the overlapping of computations and communications may be a way to reduce this drawback. We will see a implementation...

Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem

Ireneusz Czarnowski, Piotr Jędrzejowicz (2011)

International Journal of Applied Mathematics and Computer Science

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The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning...

Graphics processing units in acceleration of bandwidth selection for kernel density estimation

Witold Andrzejewski, Artur Gramacki, Jarosław Gramacki (2013)

International Journal of Applied Mathematics and Computer Science

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The Probability Density Function (PDF) is a key concept in statistics. Constructing the most adequate PDF from the observed data is still an important and interesting scientific problem, especially for large datasets. PDFs are often estimated using nonparametric data-driven methods. One of the most popular nonparametric method is the Kernel Density Estimator (KDE). However, a very serious drawback of using KDEs is the large number of calculations required to compute them, especially...

Consensus clustering with differential evolution

Miroslav Sabo (2014)

Kybernetika

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Consensus clustering algorithms are used to improve properties of traditional clustering methods, especially their accuracy and robustness. In this article, we introduce our approach that is based on a refinement of the set of initial partitions and uses differential evolution algorithm in order to find the most valid solution. Properties of the algorithm are demonstrated on four benchmark datasets.