AIS Electronic Library (AISeL) - Proceedings of the International Conference on Information Systems Development (ISD): Procedural Creation of Atmospheric Effects for Information Systems using CUDA (original) (raw)

Wiktor Sawaryn, Gdańsk University of Technology, PolandFollow
Arkadiusz Koprowski, Gdańsk University of Technology, PolandFollow
Paulina Szumała, Gdańsk University of Technology, PolandFollow
Kinga Tłuścik, Gdańsk University of Technology, PolandFollow
Grzegorz Ul, Katana Poland, PolandFollow
Bartłomiej Mróz, Gdańsk University of Technology, PolandFollow

Abstract

This paper presents a Design Science Research (DSR) approach to addressing visualization bottlenecks in environmental Information Systems Development (ISD). By developing a CUDA-based atmospheric effects framework utilizing the Material Point Method (MPM) and Marching Cubes algorithms, we demonstrate how GPU acceleration transforms ISD methodologies for data-intensive decision support systems (DSS). This research contributes to digital transformation of environmental monitoring platforms by enabling real-time processing of complex simulation data that traditionally require significant computational resources. The prototype demonstrates scalable performance handling up to 6.5 million particles while enabling configuration-driven customization that allows information systems developers to integrate sophisticated environmental visualization without specialized graphics expertise. This approach democratizes atmospheric data visualization for environmental monitoring systems. Empirical results demonstrate real-time visualization capabilities suitable for operational deployment.

DOI

10.62036/ISD.2025.142

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Procedural Creation of Atmospheric Effects for Information Systems using CUDA

This paper presents a Design Science Research (DSR) approach to addressing visualization bottlenecks in environmental Information Systems Development (ISD). By developing a CUDA-based atmospheric effects framework utilizing the Material Point Method (MPM) and Marching Cubes algorithms, we demonstrate how GPU acceleration transforms ISD methodologies for data-intensive decision support systems (DSS). This research contributes to digital transformation of environmental monitoring platforms by enabling real-time processing of complex simulation data that traditionally require significant computational resources. The prototype demonstrates scalable performance handling up to 6.5 million particles while enabling configuration-driven customization that allows information systems developers to integrate sophisticated environmental visualization without specialized graphics expertise. This approach democratizes atmospheric data visualization for environmental monitoring systems. Empirical results demonstrate real-time visualization capabilities suitable for operational deployment.