There is a large body of scientific work on numerical simulation to model physical processes such as advection and diffusion in disparate fields such as ecology, oceanography, and climate science. Unfortunately, resolving the small time and spatial scale characteristics of these processes is computationally expensive and often limits fundamental studies to short times and small areas. Moreover, as these small-scale processes may lead to emergent properties on the regional and global scales, such simulations of limited scope (either in space or time) may lead to an incorrect understanding of large-scale dynamics. This is especially true in the climate sciences. In this work we propose to bring a user-friendly framework for High Performance Computing to the desktop computing environment so that small-scale processes can be simulated over large areas and the true nature of scaling understood. In particular, this work will take advantage of multi-processor Graphics Processing Units (GPUs) to increase the speed of computational simulations by up to three orders of magnitude over conventional workstation processors. These multi-processor GPUs provide a way to distribute the graphics rendering loads in such a way that, for example, state of the art computer games are visually realistic and fast enough to allow for user interactivity. Moreover, these same GPUs that permit fast graphics rendering can also be used to significantly accelerate numerical simulations. However, while there are individual groups that have taken advantage of GPU technology, by and large most scientists have not. Thus one goal of this work is to develop software tools for GPU simulation that are both flexible enough to use across a range of scientific problems, and are easy to use for researchers who have no GPU programming experience. This project will provide simulation tools for grid-based simulations for partial differential equations and particle-based simulations.
While the technical objective of this work is to bring High Performance Computing to the desktop computing environment, it will be done so in the pursuit of scientific questions related to climate change. The investigators of this study have a history of expertise in the Arctic and the Everglades, and therefore will focus on problems relevant to these areas. The scientific goals are: (i) To better understand how processes scale. Specifically, whether small-scale non-linearities and feedbacks lead to emergent properties at scale. (ii) In the Arctic, the goal is to develop a mechanistic understanding of how snow and shrubs independently and interactively affect physical and biological controls over soil nitrogen dynamics and decomposition in arctic tundra, and how these dynamics in turn affect vegetation composition and productivity. (iii) In the Everglades, the goal is identify the dynamics that govern the formation and maintenance of the ridge and slough vegetation system and understand how management scenarios and changes in sea level and salt water intrusion will alter form and function of this patterned vegetation system.
This research has the potential for broader impact in several areas. The investigations and discoveries about the interaction between vegetation, nutrients and water motion in the Everglades may provide guidance in terms of land use and resource management in this region. The snow capture models could provide a better understanding about the interaction between climate change and vegetation in the Arctic. Finally, the tools for grid-based simulations and particle-based simulations can provide a platform for using desktop computers with GPUs in a number of other areas of scientific investigation.
