From economics to natural sciences, many disciplines use complex models and simulations to better understand the world, but the unknown parameters of these models can be difficult to find. Looking to optimise the search for such parameters, many turn to the high parallelism afforded by general purpose Graphical Processing Unit (GP-GPU) programming. This paper discusses the challenges faced and lessons learned when porting such a marine ecology simulation from a pure-CPU implementation to make use of GPU technology. While this is a specific implementation, many of the problems we encountered apply generally to GPU-based simulations. They therefore hint at the potential for reusable solutions to GPU-based environmental simulations, and pave the way for a generic GPU-middleware for natural sciences.
ACM Copyright Notice: © ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 10th International Workshop on Middleware for Clouds and e-science (MGC 2012, Montreal, Canada, December 3, 2012)..complete documentdoi: http://doi.org/10.1145/2405136.2405142 (publisher's link)