Industry trends in the coming years imply the availability of cluster computing with hundreds to thousands of cores per chip, as well as the use of accelerators. Programming presents a challenge due to this heterogeneous architecture; thus, using novel programming models that facilitate this process is necessary. In this chapter, the case of simulation and visualization of crowds is presented. The authors analyze and compare the use of two programming models: OmpSs and CUDA. OmpSs allows to take advantage of all the resources available per node by combining the CPU and GPU while automatically taking care of memory management, scheduling, communications and synchronization. Experimental results obtained from Fermi, Kepler and Maxwell GPU architectures are presented, and the different modes used for visualizing the results are described, as well.
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.
Large scale crowd simulation and visualization combine computer graphics, artificial intelligence and high performance computing among other areas. Crowd sourced location data is used to compute spatio-temporal people and vehicle flows, while map and geometric data describe specific real places. With all this data, we can visualize both real trajectories and data driven on-line crowd simulation. We have some initial results using vehicle trajectory data.
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