1 research outputs found
Improved OpenCL-based Implementation of Social Field Pedestrian Model
Two aspects of improvements are proposed for the OpenCL-based implementation
of the social field pedestrian model. In the aspect of algorithm, a method
based on the idea of divide-and-conquer is devised in order to overcome the
problem of global memory depletion when fields are of a larger size. This is of
importance for the study of finer pedestrian walking behavior, which usually
requires larger fields. In the aspect of computation, the OpenCL heterogeneous
framework is thoroughly studied. Factors that may affect the numerical
efficiency are evaluated, with regarding to the social field model previously
proposed. This includes usage of local memory, deliberate patch of data
structures for avoidance of bank conflicts, and so on. Numerical experiments
disclose that the numerical efficiency is brought to an even higher level.
Compared to the CPU model and the previous GPU model, the current GPU model can
be at most 71.56 and 13.3 times faster respectively so that it is more
qualified to be a core engine for analysis of super-large scale crowd