Using Gpus To Meet Next Generation Weather Model Computational Requirements

Abstract

Colorado Institute for Research in Environmental Sciences (CIRES), University of Colorado, 325 Broadway, Boulder, CO 80305, United States AB: Weather prediction goals within the Earth Science Research Laboratory at NOAA require significant increases in model resolution (~1 km) and forecast durations (~60 days) to support expected requirements in 5 years or less. However, meeting these goals will likely require at least 100k dedicated cores. Few systems will exist that could even run such a large problem, much less house a facility that could provide the necessary power and cooling requirements. To meet our goals we are exploring alternative technologies, including Graphics Processing Units (GPU), that could provide significantly more computational performance and reduced power and cooling requirements, at a lower cost than traditional high-performance computing solutions. Our current global numerical weather prediction model, the Flow following finite-volume Isocahedral Model (FIM, http://fim.noaa.gov), is still early in its development but is already demonstrating good fidelity and excellent scalability to 1000s of cores. The icosahedral grid has several complexities not present in more traditional Cartesian grids including polygons with different numbers of sides (five and six) and non-trivial computation of locations of neighboring grid cells. FIM uses an indirect addressing scheme that yields very compact code despite these complexities. We have extracted computational kernels that encompass functions likely to take the most time at higher resolutions including all that have horizontal dependencies. Kernels implement equations for computing anti-diffusive flux-corrected transport across cell edges, calculating forcing terms and time-step differencing, and re-computing time-dependent vertical coordinates. We are extending these kernels to explore performance of GPU-specific optimizations. We will present initial performance results from the computational kernels of the FIM model, as well as the challenges related to porting code with indirect memory references to the NVIDIA GPUs. Results of this investigation should benefit the design our next-generation isocahedral weather and climate models.