Andrés Cencerrado

Response time assessment in forest fire spread simulation: An integrated methodology for efficient exploitation of available prediction time

This work details a framework developed to shorten the time needed to perform fire spread predictions. The methodology presented relies on a two-stage prediction strategy which introduces a calibration stage in order to relieve the effects of uncertainty on simulator input parameters. Early assessment of the response time and quality of the results obtained constitute a key component in this method. This automatic and intelligent process of identification of lengthy simulations that slow down the course of the predictions presents a very high hit ratio. However, discarding certain simulations from the adjustment process (based on evolutionary algorithms) could lead to loss of accuracy in our predictions. A strong statistical study to analyze the impact of this action on our final predictions is reported. This study is based on a real fire which burnt 13,000 ha in the region of Catalonia (north-east of Spain) in the summer of 2012.

Support for Urgent Computing Based on Resource Virtualization

Virtualization technologies provide flexible execution environments that could bring important benefits for computational problems with strong deadlines. Large Grid infrastructures are becoming available nowadays and they could be a suitable environment to run such on-demand computations that might be used in decision-making processes. For these computation, we encounter the need to deliver as much resources as possible at particular times. These resources may be provided by different institutions belonging to a grid infrastructure but there are two important issues that must be satisfied. Firstly, all resources must be correctly configured and all the components needed by the application must be properly installed. If there is something small missing that is required then applications will fail. Secondly, the execution of urgent applications must be made quickly in order to produce useful results in time. If applications must wait in a queue, results might be useless because they are obtained too late. To address these issues, we describe a job management service, based on virtualization techniques, that avoids configuration problems and increases the number of available resources to run applications with critical deadlines. We describe the main components of our service that can be used on top of common batch queue systems and we show some experimental results that prove the benefits of applying time-sharing techniques on the virtual machines to increase the performance of urgent computations.

Real-time genetic spatial optimization to improve forest fire spread forecasting in high-performance computing environments

Abstract 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.

Enhancing computational efficiency on forest fire forecasting by time-aware Genetic Algorithms

A way to overcome data input uncertainty when simulating forest fire propagation, consists of calibrating inaccurate input data by applying computational-intensive methods. Genetic Algorithms (GA) are powerful and robust optimization techniques. However, their main drawback is their overall run time, which can easily become unacceptable, especially when dealing with natural disasters forecast. The prediction system has been parallelized using a hybrid MPI-OpenMP approach where the number of cores allocated to each GA individual is based on a priori time-aware population classification, which allows to keep bounding the optimization process bound to a predetermined deadline. In this work, an efficient time-aware GA is introduced that estimates the required number of cores to keep the calibration process under imposed time limits and also takes into account an efficient use of the computational resources.

Time aware genetic algorithm for forest fire propagation prediction: exploiting multi‐core platforms

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.

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.

Applying Probability Theory for the Quality Assessment of a Wildfire Spread Prediction Framework Based on Genetic Algorithms

This work presents a framework for assessing how the existing constraints at the time of attending an ongoing forest fire affect simulation results, both in terms of quality (accuracy) obtained and the time needed to make a decision. In the wildfire spread simulation and prediction area, it is essential to properly exploit the computational power offered by new computing advances. For this purpose, we rely on a two-stage prediction process to enhance the quality of traditional predictions, taking advantage of parallel computing. This strategy is based on an adjustment stage which is carried out by a well-known evolutionary technique: Genetic Algorithms. The core of this framework is evaluated according to the probability theory principles. Thus, a strong statistical study is presented and oriented towards the characterization of such an adjustment technique in order to help the operation managers deal with the two aspects previously mentioned: time and quality. The experimental work in this paper is based on a region in Spain which is one of the most prone to forest fires: El Cap de Creus.

Case Study in Large Scale Climate Simulations: Optimizing the Speedup/Efficiency Balance in Supercomputing Environments

In this work we present the EC-Earth coupled climate model, which is a seamless Earth System Model (ESM) used to carry out climate research in 24 academic institutions and meteorological services from 11 countries in Europe. This model couples several components and it is continuously under development. As a coupled model, EC-Earth consists of several different sub models, each of them presenting different degrees of performance because of their different features and complexities. This represents an important disadvantage at the time of consuming computing time in supercomputing environments, which usually is granted by the corresponding host institution provided that the efficient usage of the computing resources is demonstrated. For this reason, a study to determine the best distribution of computing processors between components was carried out to assess empirically the different performance of the model depending on the amount of computing processors allocated to each component. This experimentation allowed us to optimize the speedup/efficiency balance of the model. Moreover, the obtained results highlight the important drawback caused by the low scalability of one of the EC-Earth components.

Reducing Data Uncertainty in Forest Fire Spread Prediction: A Matter of Error Function Assessment.

Forest fires are a significant problem that every year causes important damages around the world. In order to efficiently tackle these hazards, one can rely on forest fire spread simulators. Any forest fire evolution model requires several input data parameters to describe the scenario where the fire spread is taking place, however, this data is usually subjected to high levels of uncertainty. To reduce the impact of the input-data uncertainty, different strategies have been developed during the last years. One of these strategies consists of adjusting the input parameters according to the observed evolution of the fire. This strategy emphasizes how critical is the fact of counting on reliable and solid metrics to assess the error of the computational forecasts. The aim of this work is to assess eight different error functions applied to forest fires spread simulation in order to understand their respective advantages and drawbacks, as well as to determine in which cases they are beneficial or not.

A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction

Abstract Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire propagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular conditions where the fire is evolving (vegetation conditions, meteorological conditions) to predict the fire propagation. However, these parameters are usually difficult to measure or estimate precisely, and there is a high degree of uncertainty in many of them. This uncertainty provokes a certain lack of accuracy in the predictions with the consequent risks. So, it is necessary to apply methods to reduce the uncertainty in the input parameters. This work presents a comparison of ESSIM-EA and ESSIM-DE: two methods to reduce the uncertainty in the input parameters. These methods combine Evolutionary Algorithms, Parallelism and Statistical Analysis to improve the propagation prediction.