Core Allocation Policies on Multicore Platforms to Accelerate Forest Fire Spread Predictions

Software simulators are developed to predict forest fire spread. Such simulators require several input parameters which usually are difficult to know accurately. The input data uncertainty can provoke a mismatch between the predicted forest fire spread and the actual evolution. To overcome this uncertainty a two stage prediction methodology is used. In the first stage a genetic algorithm is applied to find the input parameter set that best reproduces actual fire evolution. Afterwards, the prediction is carried out using the calibrated input parameter set. This method improves the prediction error, but increments the execution time in a context with hard time constraints. A new approach to speed up the two stage prediction methodology by exploiting multicore architectures is proposed. A hybrid MPI-OpenMP application has been developed and different allocation policies have been tested to accelerate the forest fire prediction with an efficient use of the available resources. © 2014 Springer-Verlag.