An overview of the technologies and methods is presented, on the basis of which the authors of this article model the processing of a large amount of data, developing a web application. In particular, it is proposed to combine models to improve the efficiency of processing large amounts of data. Large data set before traditional storage systems and processing a new challenge. This article analyzes possible methods their decisions, limitations that do not allow to do it effectively, and also provides an overview of three modern approaches to working with large data: NoSQL and real-time event flow processing. Analysis of large data requires the use of technology and the means to implement highly productive computing. The main factors of the problem are, first of all, the complexity and the second physical volume of the information collection. It should be noted that the actual processing of data includes the construction of the algorithm and the time for its description and debugging. Unique data collections require the development of unique algorithms, which increases the total processing time by an order of magnitude.

The article is devoted to the development of an information system for automating business processes of a modern enterprise with ensuring stability and reliability, which are implemented by the applications developed by the authors. Goal is to develop improvements to the core digitalization processes of enterprises for sustainable functioning. The authors carried out a deep analysis and described the main stages of the enterprise digitalization process: the process of document approval, business processes of personnel management, etc. The architecture of the information system, a description of business processes and the principles of reliability and fault tolerance of the system being developed have been developed. The developed desktop-client application provides connection to the information system with the help of working computers of the enterprise through a local network with access to the application server. This allows you to reduce damage from accidental or deliberate incorrect actions of users and administrators; separation of protection; a variety of means of protection; simplicity and manageability of the information system and its security system.

No abstract

In this paper, we consider numerical simulation and GPU (graphics processing unit) computing for the two-dimensional non-linear tsunami equation, which is a fundamental equation of tsunami propagation in shallow water areas. Tsunamis are highly destructive natural disasters that have a significant impact on coastal regions. These events are typically caused by undersea earthquakes, volcanic eruptions, landslides, and possibly an asteroid impact. To solve numerically, firstly we discretized these equations in a rectangular domain and then transformed the partial differential equations into semi-implicit finite difference schemes. The spatial and time derivatives are approximated by using the second-order centered differences following the Crank-Nicolson method and the calculation method is based on the Jacobi method; the computation is performed using the C++ programming language; and the visualization of numerical results is performed by Matlab 2021. The initial condition was given as a Gaussian, and the basin profile has been approximated by a hyperbolic tangent. To accelerate the sequential algorithm, a parallel computation algorithm is developed using CUDA (Compute Unified Device Architecture) technology. CUDA technology has long been used for the numerical solution of partial differential equations (PDEs). It uses the parallel computing capabilities of graphics processing units (GPUs) to speed up the PDE solution. By taking advantage of the GPU’s massive parallelism, CUDA technology can significantly speed up PDE computations, making it an effective tool for scientific computing in a variety of fields. The performance of the parallel implementation is tested by comparing the computation time between the sequential (CPU) solver and CUDA implementations for various mesh sizes. The comparison shows that our parallel implementation gives significant acceleration in the implementation of CUDA.