high performance computing on graphics processing units: hgpu.org

Numerical models have been used extensively in the last decades to understand and predict weather phenomena and the climate. In general, models are classified according to their operation domain: global (entire Earth) and regional (country, state, etc). Global models have spatial resolution of about 0.2 to 1.5 degrees of latitude and therefore cannot represent very well the scale of regional weather phenomena. Their main limitation is computing power. On the other hand, regional models have higher resolution but are restricted to limited area domains. Forecasting on limited domain demands the knowledge of future atmospheric conditions at domain’s borders. Therefore, regional models require previous execution of global models. OLAM (Ocean-Land-Atmosphere Model), initially developed at Duke University (Walko & Avissar, 2008), tries to combine these two approaches to provide a global grid that can be locally refined, forming a single grid. This feature allows simultaneous representation (and forecasting) of both the global and the local scale phenomena, as well as bi-directional interactions between scales. Due to the large computational demands and execution time constraints, these models rely on parallel processing. They are executed on clusters or grids in order to benefit from the architecture’s parallelism and divide the simulation load. On the other hand, over the next decade the degree of on-chip parallelism will significantly increase and processors will contain tens and even hundreds of cores, increasing the impact of levels of parallelism on clusters. In this scenario, it is imperative to investigate the scale of programs on multilevel parallelism environment.