Main objective of this paper is to outline possible ways how to achieve a substantial acceleration in case of advection-diffusion equation (A-DE) calculation, which is commonly used for a description of the pollutant behavior in atmosphere. A-DE is a kind of partial differential equation (PDE) and in general case it is usually solved by numerical integration due to its high complexity. These types of calculations are time consuming thus the main idea of our work is to adopt CUDA platform and commodity GPU card to do the calculations in a faster way. The solution is based on method of lines with 4 th order Runge-Kutta scheme to handle the integration. As a matter of fact, the selected approach involves number of auxiliary variables and thus the memory management is critical in order to achieve desired performance. We have implemented several possible solutions that use different memory access schemes. Detailed evaluation is provided in this paper where the obtained results show a tremendous processing speed up in comparison to CPU.
In this paper we present our solution that brings artificial agents into the world of wireless sensor networks. Architecture of agent platform, which is implemented on individual nodes of such network, is also introduced and its functionality on the conceptual level is described. Important part of the platform is a module for an agent control language interpretation. Such language is an original low level control language called Agent Low Level Language (ALLL). Other modules are responsible for data logging, agent migration and for general control of the platform execution cycle.
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