131 research outputs found

    Automatic Parallelization of a Gap Model using Java and OpenCL

    Get PDF
    International audienceNowadays, scientists are often disappointed by the outcome when parallelizing their simulations, in spite of all the tools at their disposal. They often invest much time and money, and do not obtain the expected speed-up. This can come from many factors going from a wrong parallel architecture choice to a model that simply does not present the criteria to be a good candidate for parallelization. However, when parallelization is successful, the reduced execution time can open new research perspectives, and allow to explore larger sets of parameters of a given simulation model. Thus, it is worth investing some time and workforce to figure out whether an algorithm is a good candidate to parallelization. Automatic parallelization tools can be of great help when trying to identify these properties. In this paper, we apply an automatic parallelization approach combining Java and OpenCL on an existing Gap Model. The two technologies are linked with a library from AMD called Aparapi. The latter allowed us to study the behavior of our automatically parallelized model on 10 different platforms, without modifying the source code

    The Leeway of Shipping Containers at Different Immersion Levels

    Full text link
    The leeway of 20-foot containers in typical distress conditions is established through field experiments in a Norwegian fjord and in open-ocean conditions off the coast of France with wind speed ranging from calm to 14 m/s. The experimental setup is described in detail and certain recommendations given for experiments on objects of this size. The results are compared with the leeway of a scaled-down container before the full set of measured leeway characteristics are compared with a semi-analytical model of immersed containers. Our results are broadly consistent with the semi-analytical model, but the model is found to be sensitive to choice of drag coefficient and makes no estimate of the cross-wind leeway of containers. We extend the results from the semi-analytical immersion model by extrapolating the observed leeway divergence and estimates of the experimental uncertainty to various realistic immersion levels. The sensitivity of these leeway estimates at different immersion levels are tested using a stochastic trajectory model. Search areas are found to be sensitive to the exact immersion levels, the choice of drag coefficient and somewhat less sensitive to the inclusion of leeway divergence. We further compare the search areas thus found with a range of trajectories estimated using the semi-analytical model with only perturbations to the immersion level. We find that the search areas calculated without estimates of crosswind leeway and its uncertainty will grossly underestimate the rate of expansion of the search areas. We recommend that stochastic trajectory models of container drift should account for these uncertainties by generating search areas for different immersion levels and with the uncertainties in crosswind and downwind leeway reported from our field experiments.Comment: 25 pages, 11 figures and 5 tables; Ocean Dynamics, Special Issue on Advances in Search and Rescue at Sea (2012
    corecore