Parallel computing for weather prediction

1994 ◽  
pp. 305-310
Author(s):  
Ute Gärtel ◽  
Wolfgang Joppich ◽  
Anton Schüller
Keyword(s):  
Parallel Computing ◽  
Weather Prediction

scholarly journals Artificial Neuronal Networks: A Bayesian Approach Using Parallel Computing

2018 ◽  
Vol 41 (2) ◽  
pp. 173-189 ◽  
Author(s):  
Eduardo Guzmán ◽  
Mario Vázquez ◽  
David Del Valle ◽  
Paulino Pérez-Rodríguez
Keyword(s):  
Parallel Computing ◽  
Intelligent Systems ◽  
Weather Prediction ◽  
Divide And Conquer ◽  
Biological Behavior ◽  
Processing Unit ◽  
Application Performance ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
The Brain

An Artificial Neural Network (ANN) is a learning paradigm and automatic processing inspired in the biological behavior of neurons and the brain structure. The brain is a complex system; its basic processing unit are the neurons, which are distributed massively in the brain sharing multiple connections between them. The ANNs try to emulate some characteristics of humans, and can be thought as intelligent systems that perform some tasks in a different way that actual computer does. The ANNs can be used to perform complex activities, for example: pattern recognition and classification, weather prediction, genetic values prediction, etc. The algorithms used to train the ANN, are in general complex, so therefore there is a need to have alternatives which lead to a significant reduction of times employed to train an ANN. In this work, we present an algorithm based in the strategy ``divide and conquer'' which allows to train an ANN with a single hidden layer. Part of the sub problems of the general algorithm used for training are solved by using parallel computing techniques, which allows to improve the performance of the resulting application. The proposed algorithm was implemented using the C++ programming language, and the libraries Open MPI and ScaLAPACK. We present some application examples and we asses the application performance. The results shown that it is possible to reduce significantly the time necessary to execute the program that implements the algorithm to train the ANN.

Parallel Computing in Optimization

1998 ◽  
Vol 49 (7) ◽  
pp. 770-771
Author(s):  
V J Rayward-Smith
Keyword(s):  
Parallel Computing

Matlab and Parallel Computing

2012 ◽  
Vol 17 (4) ◽  
pp. 207-216 ◽  
Author(s):  
Magdalena Szymczyk ◽  
Piotr Szymczyk
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Parallel Computing ◽  
Distributed Computing ◽  
Control Systems ◽  
High Performance ◽  
Parallel Applications ◽  
Process Simulations ◽  
Key Features ◽  
Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

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