Away to improve air quality (AQ) in urban areas consists of including more green infrastructures. To evaluate their effects, air quality simulations are employed to examine the behavior of pollutants and their dispersion patterns influenced by meteorological conditions. To study either the advantages or the drawbacks of modifying the green morphology of a city, the first step is to create a set of hypothetical green land-use maps that will later be used as input for air quality simulations. In this paper, we present an automatic green land-use generator, which uses the Monte Carlo method and Moore Neighborhood to create coherent land-use maps. These maps will be later on used to run simulations that show the impact on AQ of adding more green space to our cities.

Every year, forest fires cause damages to biodiversity, atmosphere, and economy. To face such permanent threat, wildfire analysts rely on emerging and well established technologies to determine fire behavior and propagation patterns. Nevertheless, input data describing fire scenarios are subject to high levels of uncertainty that represent a serious challenge for the correctness of the prediction. The unknown parameters need to be adjusted, and an input data calibration phase is carried over following a genetic algorithm strategy. The calibrated input is then pipelined into the actual prediction phase. In addition, this two-stage prediction scheme is leveraged by the cloud computing, which enables high level of parallelism on demand, almost real-time elasticity and unlimited scalability. All of them at a low-cost strategy. In this paper, to obtain more accurate prediction results and efficient use of cloud resources in the compute-intensive calibration phase, we propose a new fitness function in tandem with a strict deadline policy that decreases overall processing time. In consonance with the hard-deadline-driven nature of fire extinction activities, the proposed strategies improve the genetic algorithm convergence and decrease the response time for the calibration stage, setting up an important upper bound limit to the critical compute-intensive adjustment phase. For the case study evaluated, only 3.87% of of accuracy loss is given out in exchange of a guarantee that the calibration phase will never last more than 50 minutes in the worst case.

Forest fires severely affect many ecosystems every year, leading to large environmental damages, casualties, and economic losses. Emerging and established technologies are used to help wildfire analysts determine fire behavior and spread, aiming at more accurate prediction results and efficient use of resources in fire fighting. We propose a novel forest fire spread prediction platform based on a proven two-stage prediction model devised to deal with input data uncertainties. The model is able to calibrate the unknown parameters based on the real observed data using an iterative process. Since this calibration is compute-intensive and due to the unpredictability of urgent computing needs, we exploit an elastic and scalable cloud-based solution platform implemented through coarse-grain parallel processing using a work queue.

Advances in high-performance computing have led to an improvement in modeling multi-physics systems because of the capacity to solve complex numerical systems in a reasonable time. WRF–SFIRE is a multi-physics system that couples the atmospheric model WRF and the forest fire spread model called SFIRE with the objective of considering the atmosphere–fire interactions. In systems like WRF–SFIRE, the trade-off between result accuracy and time required to deliver that result is crucial. So, in this work, we analyze the influence of WRF–SFIRE settings (grid resolutions) into the forecasts accuracy and into the execution times on multi-core platforms using OpenMP and MPI parallel programming paradigms.

Wind field is a critical issue in forest fire propagation prediction. However, wind field calculation is a complex problem that, for large terrains, involves solving huge linear systems. To solve such systems, the Preconditioned Conjugate Gradient (PCG) solver is applied. SSOR and Jacobi preconditioners are usually used, but solving such systems takes too much time and makes the approach unfeasible in real time operation. Parallelization appears as a way to make the approach operational in real time. The PCG with both preconditioners has been parallelized to accelerate the execution. However, the improvement in execution time is not enough, and the Schwarz alternating domain decomposition has been applied to exploit a second level of parallelism. Using this method, the linear system is decomposed in a set of overlapped subdomains that can be solved in parallel using a Master/Worker paradigm, where each worker exploits the PCG solver parallelism. As a result, the wind field calculation time is significantly reduced; for example, a large map of 1200×1200 cells, whose solution took more than 2000 seconds in the original WindNinja, can now be solved in less than 240 seconds using 4 subdomain and 4 cores per subdomain.

Summary Forest fire propagation prediction is a crucial issue when fighting these hazards as efficiently as possible. Several propagation models have been developed and integrated in computer simulators. Such models require a set of input parameters that, in some cases, are difficult to know or even estimate precisely beforehand. Therefore, a calibration technique based on genetic algorithm (GA) was introduced to reduce the uncertainty in input parameters values and improve the accuracy of the predictions. Such a technique requires the execution of a set of simulations and several iterations of the process to calibrate the values of the input parameters. To reduce the execution time of this calibration stage, an Message Passing Interface master/worker scheme was developed to distribute the simulations of one iteration among the worker processes. However, the execution time of each simulation varies drastically depending on the particular input parameters used, provoking a significant load imbalance. To overcome this imbalance and reduce execution time to operational requirements, core allocation policies have been developed. These policies are based on execution time estimation and classification of simulations according to the estimated execution time. Then, multicore capabilities of the current systems are applied to devote more resources (cores) to the longest simulations reducing the load imbalance. These simulations that are estimated as taking too long, even when many resources are devoted to them, require especial consideration. So, a generation time limit has been introduced, and three different strategies have been designed considering individuals that exceed the generation execution time limit. In the first one, the longest individuals are replaced before starting the execution with shorter individuals (Time Aware Core allocation with replacement). In the second one, these individuals are executed, but when the generation limit is reached, the individuals still executing are killed (Time Aware Core allocation without replacement). In the third one, all the individuals are executed normally, and when the generation time limit is reached, the GA is applied considering the individuals that have finished their executions, while the individuals still executing are allowed to continue running and are considered by the GA when they finish. The three strategies have been tested in real scenarios, and the results show these policies significantly improve the calibration accuracy within the superimposed deadlines. © 2016 The Authors.Concurrency and Computation: Practice and ExperiencePublished by John Wiley & Sons Ltd.

Wind field calculation is a critical issue in reaching accurate forest fire propagation predictions. However, when the involved terrain map is large, the amount of memory and the execution time can prevent them from being useful in an operational environment. Wind field calculation involves sparse matrices that are usually stored in CSR storage format. This storage format can cause sparse matrix-vector multiplications to create a bottleneck due to the number of cache misses involved. Moreover, the matrices involved are extremely sparse and follow a very well-defined pattern. Therefore, a new storage system has been designed to reduce memory requirements and cache misses in this particular sparse matrix-vector multiplication. Sparse matrix-vector multiplication has been implemented using this new storage format and taking advantage of the inherent parallelism of the operation. The new method has been implemented in OpenMP, MPI and CUDA and has been tested on different hardware configurations. The results are very promising and the execution time and memory requirements are significantly reduced.

Every year wildfires are responsible of the loss of thousands of hactares of European forest, millions of dollars in damage and, in the worst case, for loss of human lives. Accurate observation of wildland fire evolution is a crucial issue to estimate the burned area, to use the observed data in propagation prediction and to calibrate input parameters of propagation models. In this direction the European Forest Fire Information System (EFFIS) supports the services in charge of the protection of European forests. Satellite images provide useful information, but, in many cases, the clouds or the fire smoke itself do not allow to have a good estimation of the fire front position. So, a methodology based on thermal anomalies information has been applied to estimate fire perimeters even on adverse conditions. This methodology has been recently added to EFFIS to determine the perimeter evolution of a wildfire using thermal anomalies information coming from NASA's satellites. In this paper we describe the algorithm to perform this task and how it was implemented and integrated into the EFFIS services.

Forest fires are a kind of natural hazard with a high number of occurrences in southern European countries. To avoid major damages and to improve forest fire management, one can use forest fire spread simulators to predict fire behavior. When providing forest fire predictions, there are two main considerations: accuracy and computation time. In the context of natural hazards simulation, it is well known that part of the final forecast error comes from uncertainty in the input data. These data typically consist of a set of GIS files, which should be appropriately conflated. For this reason, several input data calibration methods have been developed by the scientific community. In this work, the Two-Stage calibration methodology, which has been shown to provide good results, is used. This calibration strategy is computationally intensive and time-consuming because it uses a Genetic Algorithm as a solution. Taking into account the aspect of urgency in forest fire spread prediction, it is necessary to maintain a balance between accuracy and the time needed to calibrate the input parameters. In order to take advantage of this technique, one must deal with the problem that some of the obtained solutions are impractical, since they involve simulation times that are too long, preventing the prediction system from being deployed at an operational level. A new method which finds the minimum resolution reduction for such long simulations, keeping accuracy loss to a known interval, is proposed. The proposed improvement is based on a time-aware core allocation policy that enables real-time forest fire spread forecasting. The final prediction system is a cyberinfrastructure, which enables forest fire spread prediction at real time.

• Predicting forest fire propagation is a crucial issue to mitigate fire damages. • Wind significantly affects forest fire propagation. • Wind field calculation implies solving extremely large systems of equations. • The Schur method has been applied to accelerate wind field calculation. • Execution time is reduced to accomplish operational requirements. Forest fire are natural hazards that every year cause significant looses. Predicting the evolution of a forest fire is a critical issue in mitigating its effects. Such predictions must accomplish strict real time constraints to be effective. Wind field calculation is a key issue in providing accurate forest fire propagation predictions. However, it implies solving large linear systems with 10 5 to 10 8 variables that takes too long using conventional methods. Therefore, the domain decomposition Schur method has been applied to accelerate wind field calculation. Using the Schur method, the linear system is significantly reduced and several phases can be parallelised exploiting cluster computing capabilities. Results show that the execution time for the wind field calculation of a map of 800 × 800 cells has been reduced from 400 s to 90 s using 10 nodes.

In our research work, we use two Dynamic Data Driven Application System (DDDAS) methodologies to predict wildfire propagation. Our goal is to build a system that dynamically adapts to constant changes in environmental conditions when a hazard occurs and under strict real-time deadlines. For this purpose, we are on the way of building a parallel wildfire prediction method, which is able to assimilate real-time data to be injected in the prediction process at execution time. In this paper, we propose a strategy for data injection in distributed environments.

The need for input parameter optimisation in environmental modelling is a long-known and very time-consuming task. However, to avoid tragedy, disaster propagation predictions have to satisfy hard real-time constraints. Especially small disaster control centres with limited computing resources require fast and efficient calibration methods to deliver reliable predictions in time. The combination of a clustering method together with a Genetic Algorithm is used as parameter optimisation technique in forest fire spread prediction. We formalise and demonstrate the potential of the resulting Evolutionary Intelligent System's architecture to solve the complex problem of input parameter calibration on restricted simulation conditions.

Run-time tools are crucial to program development. In our desktop computer environments, we take for granted the availability of tools for operations such as debugging, profiling, tracing, checkpointing, and visualization. When programs move into distributed or Grid environments, it is difficult to find such tools. This difficulty is caused by the complex interactions necessary between application program, operating system and layers of job scheduling and process management software. As a result, each run-time tool must be individually ported to run under a particular job management system; for m tools and n environments, the problem becomes an m \times n effort, rather than the hoped-for m + n effort. Variations in underlying operating systems can make this problem even worse. The consequence of this situation is a paucity of tools in distributed and Grid computing environments. In response to the problem, we have analyzed a variety of job scheduling environments and run-time tools to better understand their interactions. From this analysis, we isolated what we believe are the essential interactions between the run-time tool, job scheduler and resource manager, and application program. We are proposing a standard interface, called the Tool Dæmon Protocol (TDP) that codifies these interactions and provides the necessary communication functions. We have implemented a pilot TDP library and experimented with Parador, a prototype using the Paradyn Parallel Performance tools profiling jobs running under the Condor batch-scheduling environment.

We present a new fully distributed dynamic load balancing algorithm called DASUD (Diffusion Algorithm Searching Unbalanced Domains). Since DASUD is iterative and runs in an asynchronous way, a mathematical model that describes DASUD behaviour has been proposed and has been used to prove DASUD's convergence. DASUD has been evaluated by comparison with another well known strategy from the literature, namely, the SID (Sender Initiated Diffusion) algorithm. The comparison was carried out by considering a large set of load distributions which were applied to ring, torus and hypercube topologies, and the number of processors ranged from 8 to 128. From these experiments we have observed that DASUD outperforms the SID strategy as it provides the best trade-off between the global balance degree obtained at the final state and the number of iterations required to reach such a state.