Parallel computing technologies in video stabilization for teaching purposes

[Abstract] In this paper, the development of a video-stabilization program is described as part of the training in the subject Parallel Architectures in the Degree in Computer Engineering of the University of Vigo. The purpose is to take advantage of the parallelism methodologies in processors to teach students about computer vision and use it in applications like vibration sensors for maintenance in Industry 4.0 or computer vision for the autonomous vehicle. The main tool to implement this program is the C programming language and the OpenCV libraryMinisterio de Economía y Competitividad; Project No 586035- EPP-1-2017-1-DZ-EPPKA2-CBHE-JPMinisterio de Ciencia y Tecnología; DPI2016-79278-C2-2-

Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks

In order to gain knowledge from large databases, scalable data mining technologies are needed. Data are captured on a large scale and thus databases are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach.are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach

IMPLEMENTATION OF THE DIFFERENCE SCHEME FOR ABSORPTION EQUATION TYPE PROBLEMS APPLYING PARALLEL COMPUTING TECHNOLOGIES

This paper describes a way of parallel algorithm technology usage for analyzing physical processes parabolic differential problems on the surface. This analysis determine the temperature distribution on the surface. Such analysis can fit calculation of Maxwell and Maxwell-Stokes equations and can be focused on mathematical models that can be reduced to the absorption or diffusion-convection-reaction equations with the initial and boundary conditions of different order (1st, 2nd, 3rd order of boundary conditions). Parallel computing technologies usage provides an acceleration possibilities of mentioned calculations in different way and effect depending of parallel technology type and method combinations used during the calculations

Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction

CALCULATION OF MATRIX CORRESPONDENCE WITH THE USE OF PARALLEL COMPUTING TECHNOLOGIES

Increasing the number of vehicles has led to urban congestion, many hours of traffic jams, obstruction of pedestrian traffic, increase the number of accidents, etc. Therefore, the importance of gaining the optimum network planning, improved traffic management, optimization of the system of public transport routes. The solution of such problems is impossible without mathematical modeling of traffic flows. An important task of modeling is to calculate the trip distribution. In this paper, we develop a program for calculating trip distribution using parallel computing technologies. The application of these technologies will improve the efficiency of simulation, increase accuracy and speed of the algorithm

Advances in thermal hydraulic and neutronic simulation for reactor analysis and safety

This paper describes several large-scale computational models developed at Argonne National Laboratory for the simulation and analysis of thermal-hydraulic and neutronic events in nuclear reactors and nuclear power plants. The impact of advanced parallel computing technologies on these computational models is emphasized

Parallel computing for numerical calculations of step-index optical fibers eigenmodes by collocation method

© 2014 IEEE. We study natural modes of weakly guiding optical fibers. The original problem is reduced to a nonlinear nonselfadjoint spectral problem for the set of weakly singular boundary integral equations. The integral operator is approximated by collocation method. We propose to use the singular value decomposition of the collocation method's matrix for the initial approximation of eigenvalues. We implement parallel computing technologies (OpenMP and MPI) using a compact supercomputer

GPU-powered Simulation Methodologies for Biological Systems

The study of biological systems witnessed a pervasive cross-fertilization between experimental investigation and computational methods. This gave rise to the development of new methodologies, able to tackle the complexity of biological systems in a quantitative manner. Computer algorithms allow to faithfully reproduce the dynamics of the corresponding biological system, and, at the price of a large number of simulations, it is possible to extensively investigate the system functioning across a wide spectrum of natural conditions. To enable multiple analysis in parallel, using cheap, diffused and highly efficient multi-core devices we developed GPU-powered simulation algorithms for stochastic, deterministic and hybrid modeling approaches, so that also users with no knowledge of GPUs hardware and programming can easily access the computing power of graphics engines.Comment: In Proceedings Wivace 2013, arXiv:1309.712