Determining map partitioning to accelerate wind field calculation

Abstract

Wind speed and direction are parameters that affect forest fire propagation dramatically. So, an accurate estimation of such parameters is crucial to predict the fire propagation precisely. WindNInja is a wind field simulator that can easily be coupled to a forest fire propagation simulator such as FARSITE. However, wind field simulators present to main drawbacks: They take too much time to compute the wind field and they require a lot of memory. So, a map partitioning strategy has been developed to compute partial wind field maps that can be aggregated afterwards. Each map part can be computed in parallel and the amount of memory required is available in a single node. In this work a methodology to determine the most adequate map partitioning is presented. The map part shape, map part size, amount of overlapping and number of parts have been studied considering execution time and effects on wind field estimation. The results are based on a wide experimentation and are validated with real case scenarios.

Predicting dispersion of radionuclides through parallel approach

“…Promote the program was refined in 2015, to diminish tedious by adjusting wind field module WindNinja. Indeed, even today the tedious procedure is an open issue, and still further review is finished by Sanjuan et al (2014) and proposed Schur technique (Hofman et al, 2011) for decaying wind field module. In PC framework parallel registering is connected broadly in applications, for example, atomic designing, and scattering of radionuclide (Holmes and Morawska, 2006) are examined.…”

“…Different forward scattering and in reverse scattering issues have been unravelled utilising SILAM scattering demonstrating. For portraying tracers in the climate because of transport and dispersion Lagrangian molecule show is regularly utilised (Saenko et al, 2011;Sanjuan et al, 2014Sanjuan et al, , 2016. The quantity of particles in a single discharge by a model is frequently in the request of 10,000 to 100,000 and the time advance for the coordination of differential conditions ranges from a couple to many seconds (Sofiev et al, 2006).…”

“…To speedup wind field figuring's, the primary GPU based approach we found in (Sanjuan et al, 2014). Our basic responsibility is to diminish planning time of the structure insinuated by Brazilian NPPs, to allow watch grained reenactments to be executed dynamically.…”

Applying vectorization of diagonal sparse matrix to accelerate wind field calculation

“…This table also shows the amount of memory (memory) required to store matrices corresponding to different map sizes. The memory required by WindNinja to store and solve the linear system depends on the map size [11]. Actually, the amount of memory required to solve a map of N × M cells can be expressed as shown in Eq.…”