No abstract

Crowd simulation has been used in the entertainment industry to create populated environments that seem more realistic than isolated scenes, specially in urban areas. Video games are no exception, but the simulation of large crowds is often restrained by the remaining compute time available in CPUs, which is often used for most part of the game loop. This paper presents a parallel technique, using consumer grade graphics hardware, for proximity queries that is suitable for real-time crowd simulations. We use a truncated Voronoi diagram and a sampling technique, using ray marching, to find agents' neighbors in an environment texture. The experimental results suggest that our technique has significantly better performance than similar methods and can achieve simulations with thousands of agents in interactive frame rate.

In this work, we analyze the implications and results of implementing dynamic parallelism, concurrent kernels and CUDA Graphs to solve task-oriented problems. As a benchmark we propose three different methods for solving DGEMM operation on tiled-matrices; which might be the most popular benchmark for performance analysis. For the algorithms that we study, we present significant differences in terms of data dependencies, synchronization and granularity. The main contribution of this work is determining which of the previous approaches work better for having multiple task running concurrently in a single GPU, as well as stating the main limitations and benefits of every technique. Using dynamic parallelism and CUDA Streams we were able to achieve up to 30% speedups and for CUDA Graph API up to 25x acceleration outperforming state of the art results.

We present a set of algorithms for simulating and visualizing real-time crowds in GPU (Graphics Processing Units) clusters. First we present crowd simulation and rendering techniques that take advantage of single GPU machines. Then, using as an example a wandering crowd behavior simulation algorithm, we explain how this kind of algorithms can be extended for their use in GPU cluster environments. We also present a visualization architecture that renders the simulation results using detailed 3D virtual characters. This architecture is adaptable in order to support the Barcelona Supercomputing Center (BSC) infrastructure. The results show that our algorithms are scalable in different hardware platforms including embedded systems, desktop GPUs, and GPU clusters, in particular, the BSC's Minotauro cluster.

Crowd simulation and visualization is an emergent research area that studies and reproduces this phenomenon on virtual environments. We present a system designed for simulation and visualization of pedestrians in urban environments, this system is focused on solving problems such as rendering, character animation, artificial intelligence to deal with steering behaviors and motion planning of each pedestrian. The present work incorporates steering pedestrian behaviors using real data collected from several sources such as GPS traces and surveillance systems, all these data allows virtual characters to adapt their navigation according to several environment factors such as other agents or obstacles. This kind of approach requires massive quantities of resources, such as data, memory and computation. We present a formulation that includes a multi-agent system that assigns individual characteristics, both physical and psychological to the virtual agents, which are based on data obtained from real pedestrians to better depict reality. The presented system is able to simulate crowds in complex urban environments; for that purpose the system was built in two stages, urban environment generation and pedestrian simulation, for the first stage we integrate the WRLD3D plug-in with real data collected from GPS traces, then we use a hybrid approach done by incorporating steering pedestrian behaviors with the goal of simulating the subtle variations present in real scenarios without needing large amounts of data for those low-level behaviors, such as pedestrian motion affected by other agents and static obstacles nearby.