Арифметика на эллиптических кривых с использованием графических вычислителей
We consider different parallel algortihms for operations in prime fields and their applications for operations on points of elliptic curves. The work provides results for implementations of these algorithms on NVIDIA graphical processors.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.
This paper describes aspects of development of decision support system based on neural networks and a genetic algorithm. We justify the use of general-purpose computing on graphics processing units (GPGPU) for our decision support system. Example of CUDA successful application to increase computing performance of the system in question is presented.
In this article we ground some advantages of the evolutionary approach to the solution of problems of decision support system development. The most popular methods of forecasting and detection of dependences are considered. Advantages of use of neural networks to forecast and to determine of dependences between parameters of systems are given. Advantages of interval neural networks are considered. Methods of finding of optimal input parameters for neural networks are appreciated. Realization of decision-making support systems with use of genetic algorithm and neural networks is described. The main advantages of parallelization of the general purpose calculations with use of the graphics processing units are listed. The realized system shell based on communication of neural networks and genetic algorithm, and optimized at the expense of use of general-purpose graphics processing units is described.
A form for an unbiased estimate of the coefficient of determination of a linear regression model is obtained. It is calculated by using a sample from a multivariate normal distribution. This estimate is proposed as an alternative criterion for a choice of regression factors.