Title :
Acceleration of a Meteorological Limited Area Model with Dataflow Engines
Author :
Oriato, Diego ; Tilbury, Simon ; Marrocu, Marino ; Pusceddu, Gabriella
Author_Institution :
Applic. Accel., Maxeler Technol., London, UK
Abstract :
Climate and weather modeling is a significant consumer of High Performance Computing due to the hard deadlines inherent in predicting weather. Given the large data volumes and runtimes involved, climate and weather modeling is ideally suited for dataflow computation. In this paper, we demonstrate a dataflow implementation of the dynamic core of a meteorological limited area model. To achieve maximum performance we transform the computation by reordering operations and encoding the data. We present results for a domain of 13,600 x 3,333 × 30 km with 620 thousand grid points, and show speedups of up to 74x comparing an x86 CPU node to a dataflow node.
Keywords :
data flow computing; search engines; CPU node; climate modeling; data volumes; dataflow engines; dynamic core; high performance computing; meteorological limited area model acceleration; reordering operations; weather modeling; Atmospheric modeling; Computational modeling; Equations; Mathematical model; Meteorology; Numerical models; Software; acceleration; climate; dataflow; hydrostatic; meteorology; paralellism; performance; weather;
Conference_Titel :
Application Accelerators in High Performance Computing (SAAHPC), 2012 Symposium on
Conference_Location :
Chicago IL
Print_ISBN :
978-1-4673-2882-1
DOI :
10.1109/SAAHPC.2012.8
