Simulation of the student enrollment system through the reactiondiffusion model and parallel computing with CUDA The reduction of student segregation should be one of the priorities of any state in terms of educational legislation and regulation since there is empirical evidence that students belonging to families in vulnerable situations, who attend less segregated schools, tend to have better academic results. In this work, we implement the reactiondiffusion model to simulate decentralized student enrollment. Reactiondiffusion equations are used in conjunction with the finite volumes method to generate a discrete domain of space over which students search for a suitable school and the explicit Euler method for the approximation of time. The computational implementation, through CUDA, allows solving for the problem of wide territories, such as cities, taking advantage of the performance benefits offered by parallel computing by GPU at a low cost. In our tests, simulations have a low runtime, converging to the total assignment of students to schools.

Distributed Simulation of Crowds with Groups in CrowdSim Simulating large crowds of individuals is socially important, e.g., for developing and studying evacuation or rescuing in dangerous situations. Such simulations remain complex due to the scalability challenge: simulating thousands of virtual characters is computationally expensive, especially when taking into account psychological factors and groupspecific behavior that play a crucial role, e.g., in panic situations and highly crowded environments. In this paper, we make two new contributions: 1) we extend the HiDAC agentbased modeling approach with the aspects of group formation and movement, and 2) we implement our approach within the CrowdSim system, including the possibility to distribute the simulation process across several compute servers for better performance. We report experimental results on scaling the distributed simulation of a realworld evacuation scenario in a building using several compute servers.