That can be exploited by distributed applications. The huge number of cores existing in current Graphics Processor Units (GPUs) provides these devices with computing capabilities Therefore, the simulations are significantly accelerated, and the system throughput and scalability are improved. Since interactivity is a hard constraint in crowd simulations, this acceleration of the collision check process represents a significant improvement in the overall system throughput and response time. The evaluation results show that the GPU-based implementation consumes less energy and provides a minimum speedup of 45× with respect to any of the CPU-based implementations. In addition, we analyse the efficiency of the different implementations taking into account the theoretical performance and power consumption of each platform. On the other hand, the comparison shows that the GPU greatly accelerates the collision test with respect to any other implementation optimized for multi-core CPUs. On the one hand, the comparison study shows the first performance evaluation of RCU in a real user-space application with complex data structures. We perform a comparison study of these different implementations.
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In order to fairly compare the GPU with the multi-core implementations, we propose a parallel CPU version that uses read-copy update (RCU), a new synchronization method which significantly improves performance. As for the many-core implementations, we analyse the bottlenecks of a previous GPU version of the collision check algorithm, proposing a new GPU version that removes the bottlenecks detected. These strategies are designed for exploiting the parallelism in both multi-core and many-core architectures like graphic processing units (GPUs). the collision check procedure that takes place in agent-based simulations. In this paper, we propose different parallelization strategies for. However, improving the scalability of crowd simulation systems by exploiting the inherent parallelism of these architectures is still an open issue. The computing capabilities of current multi-core and many-core architectures have been used in crowd simulations for both enhancing crowd rendering and simulating continuum crowds. In fact, temporal orientation has been recognized as one of the fundamental parameters of individual differences (Bluedorn & Denhardt, 1988).
It may prove challenging to one individual and debilitating to another. It is likely that time pressure does not affect everyone in the same way.
Perhaps the most substantial challenge facing employees as a result of this time orientation is trying to choose between alternative courses of action while under this time pressure. This accelerated time orientation affects every aspect of a work organization. Computer hardware operations can be carried out as fast as billionths of a second, and this capability has set new temporal standards of organizational effectiveness and efficiency. At the close of the twentieth century, life revolves ever more around the clock, especially in Western cultures Rifkil, in his book Time Wars (1987), proposes that computers are contributing to an exponentially accelerating time orientation. Since the beginning of the Industrial Revolution, the tempo of life has continually accelerated (McGrath & Kelly, 1986). Therefore, this system architecture provides the scalability required for large-scale crowd simulation. The evaluation results show that the proposed architecture is able to fully exploit the underlying hardware platform, regardless of both the number and the kind of computers that form the sys-tem. In this way, the system bottleneck is removed, and new Action Servers (hosted each one on a new computer) can be added as necessary.
This proposal consists of enhanc-ing a previously proposed architecture through the efficient parallelization of the Action Server and the distribution of the semantic database. In this paper, we propose a scalable architecture that can manage large crowds of autonomous agents at interactive rates.
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Although several proposals have focused on the software architectures for these systems, the scalability of crowd simulation is still an open issue. These applications require both rendering visually plausible images of the virtual world and managing the behavior of autonomous agents. Resumen- Crowd simulation can be considered as a special case of Virtual Environments where avatars are intelligent agents instead of user-driven entities.
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