Discrete Event Simulation (DES) allows the modelling of ever more complex systems in a variety of domains ranging from biological systems to road networks. The increasing need to model larger systems stresses the demand for efficient parallel implementations of DES engines. Recently, Graphics Processing Units have emerged as an efficient alternative to Central Processing Units for the computation of some problems. Although substantial speedups can be achieved by using GPUs, writing an efficient implementations of given suitable problems often requires in-depth knowledge of the architecture. We present a new framework integrated in the Simian engine, which allows to make efficient use of GPUs for computationally intense sections of code. This framework allows modellers to offset some or all handlers to the GPU by efficiently grouping and scheduling these handlers. As a case-study, we implement a population activity simulation that takes into account evolving traffic conditions in a simulated urban area.
['Guillaume Chapuis', 'Stephan Eidenbenz', 'Nandakishore Santhi', 'Eun Jung Park']
Simian integrated framework for parallel discrete event simulation on GPUs