Giuseppe Filippone

Accelerating wildfire susceptibility mapping through GPGPU

In the field of wildfire risk management the so-called burn probability maps (BPMs) are increasingly used with the aim of estimating the probability of each point of a landscape to be burned under certain environmental conditions. Such BPMs are usually computed through the explicit simulation of thousands of fires using fast and accurate models. However, even adopting the most optimized algorithms, the building of simulation-based BPMs for large areas results in a highly intensive computational process that makes mandatory the use of high performance computing. In this paper, General-Purpose Computation with Graphics Processing Units (GPGPU) is applied, in conjunction with a wildfire simulation model based on the Cellular Automata approach, to the process of BPM building. Using three different GPGPU devices, the paper illustrates several implementation strategies to speedup the overall mapping process and discusses some numerical results obtained on a real landscape.

Cellular Automata and GPGPU: An Application to Lava Flow Modeling

This paper presents an efficient implementation of the SCIARA Cellular Automata computational model for simulating lava flows using the Compute Unified Device Architecture CUDA interface developed by NVIDIA and carried out on Graphical Processing Units GPU. GPUs are specifically designated for efficiently processing graphic data sets. However, they are also recently being exploited for achieving excellent computational results for applications non-directly connected with Computer Graphics. The authors show an implementation of SCIARA and present results referred to a Tesla GPU computing processor, a NVIDIA device specifically designed for High Performance Computing, and a Geforce GT 330M commodity graphic card. Their carried out experiments show that significant performance improvements are achieved, over a factor of 100, depending on the problem size and type of performed memory optimization. Experiments have confirmed the effectiveness and validity of adopting graphics hardware as an alternative to expensive hardware solutions, such as cluster or multi-core machines, for the implementation of Cellular Automata models.

Efficient application of GPGPU for lava flow hazard mapping

The individuation of areas that are more likely to be impacted by new events in volcanic regions is of fundamental relevance for mitigating possible consequences, both in terms of loss of human lives and material properties. For this purpose, the lava flow hazard maps are increasingly used to evaluate, for each point of a map, the probability of being impacted by a future lava event. Typically, these maps are computed by relying on an adequate knowledge about the volcano, assessed by an accurate analysis of its past behavior, together with the explicit simulation of thousands of hypothetical events, performed by a reliable computational model. In this paper, General-Purpose Computation with Graphics Processing Units (GPGPU) is applied, in conjunction with the SCIARA lava flow Cellular Automata model, to the process of building the lava invasion maps. Using different GPGPU devices, the paper illustrates some different implementation strategies and discusses numerical results obtained for a case study at Mt. Etna (Italy), Europe’s most active volcano.

Morphological Coevolution for Fluid Dynamical-Related Risk Mitigation

In the lava flow mitigation context, the determination of areas exposed to volcanic risk is crucial for diminishing consequences in terms of human causalities and damages of material properties. In order to mitigate the destructive effects of lava flows along volcanic slopes, the building and positioning of artificial barriers is fundamental for controlling and slowing down the lava flow advance. In this article, an evolutionary computation-based decision support system for defining and optimizing volcanic hazard mitigation interventions is proposed. In particular, the SCIARA-fv2 Cellular Automata numerical model has been applied for simulating lava flows at Mt. Etna (Italy) volcano and Parallel Genetic Algorithms (PGA) adopted for optimizing protective measures construction by morphological evolution. The PGA application regarded the optimization of the position, orientation, and extension of earth barriers built to protect Rifugio Sapienza, a touristic facility located near the summit of the volcano. A preliminary release of the algorithm, called single barrier (SBA) approach, was initially considered. Subsequently, a second GA strategy, called Evolutionary Greedy Strategy (EGS), was implemented by introducing multibarrier protection measures in order to improve the efficiency of the final solution. Finally, a Coevolutionary Cooperative Strategy (CCS), has been introduced where all barriers are encoded in the genotype and, because all the constituents parts of the solution interact with the GA environment, a mechanism of cooperation between individuals has been favored. The study has produced extremely positive results and represents, to our knowledge, the first application of morphological evolution for lava flow mitigation.

CUDA Dynamic Active Thread List Strategy to Accelerate Debris Flow Simulations

Cellular Automata represent a formal frame for dynamical systems which evolve on the base of local interactions. We here present first results of the CUDA parallelization of the SCIDDICA S3-hex Complex Cellular Automata model for simulating debris flows. In particular, a first strategy for the parallelization of the model is based on a straightforward one thread - one cell approach, where each cell in the cellular space is computed by a CUDA thread. A second approach concerns the adoption of a list of CA computational active cells which is handled step by step by an efficient stream compaction algorithm, in order to reduce the excessive use of computationally inactive threads. First results performed on different graphic processors have shown that, by adopting the different CUDA strategies, this kind of hardware can be effective for landslide risk mitigation.

The new SCIARA-fv3 numerical model and acceleration by GPGPU strategies

This paper presents the parallel implementation, using the Compute Unified Device Architecture (CUDA) architecture, of the SCIARA-fv3 Complex Cellular Automata model for simulating lava flows. The computational model is based on a Bingham-like rheology and both flow velocity and the physical time corresponding to a computational step have been made explicit. The parallelization design has involved, among other issues, the application of strategies that can avoid incorrect computation results due to race conditions and achieving the best performance and occupancy of the underlying available hardware. Two hardware types were adopted for testing different versions of the CUDA implementations of the SCIARA-fv3 model, namely the GTX 580 and GTX 680 graphic processors. Despite its computational complexity, carried out experiments of the model parallelization have shown significant performance improvements, confirming that graphic hardware can represent a valid solution for the implementation of Cellular Automata models.

A Multi-GPU Approach to Fast Wildfire Hazard Mapping

Burn probability maps (BPMs) are among the most effective tools to support strategic wildfire and fuels management. In such maps, an estimate of the probability to be burned by a wildfire is assigned to each point of a raster landscape. A typical approach to build BPMs is based on the explicit propagation of thousands of fires using accurate simulation models. However, given the high number of required simulations, for a large area such a processing usually requires high performance computing. In this paper, we propose a multi-GPU approach for accelerating the process of BPM building. The paper illustrates some alternative implementation strategies and discusses the achieved speedups on a real landscape.

Swii2, a HTML5/WebGL Application for Cellular Automata Debris Flows Simulation

We here present the preliminary release of Swii2, a web application for debris flows simulation. The core of the system is Sciddica-k0, the latest release of the Sciddica debris flow Cellular Automata family, already successfully applied to the 1997 Albano lake (Italy) debris flow. In Swii2, the Sciddica-k0 model runs server-side, while a Web 2.0 application controls the simulation. The graphical user interface is based on HTML5 and JavaScript, which permits to have a fully portable application. The client is able to control the basic Sciddica-k0 simulation functionalities thanks to asynchronous callbacks to the server. Simulation results are visualized in real time by means of a 3D interactive visualization system based on WebGL, a cross-platform application program interface used to create 3D graphics in Web browsers. Eventually, user-oriented cooperative services, which desktop applications in general do not offer, are conjectured and discussed.

Evolving Protection Measures for Lava Risk Management Decision Making

Many volcanic areas around the World are densely populated and urbanized. For instance , Mount Etna (Italy) is home to approximately one million people, despite being the most active volcano in Europe. Mapping both the physical threat and the exposure and vulnerability of people and material properties to volcanic hazards can help local authorities to guide decisions about where to locate a priori critical infrastructures (e.g. hospitals, power plants, railroads, etc.) and human settlements and to devise for existing locations and facilities appropriate mitigation measures. We here present the application of Parallel Genetic Algorithms for optimizing earth barriers construction by morphological evolution, to divert a case study lava flow that is simulated by the numerical Cellular Automata model Sciara-fv2 at Mt Etna volcano (Sicily, Italy). The devised area regards Rifugio Sapienza, a touristic facility located near the summit of the volcano, where the methodology was applied for the optimization of the position, orientation and extension of an earth barrier built to protect the zone. The study has produced extremely positive results, providing insights and scenarios for the area representing, to our knowledge, the first application of morphological evolution for lava flow mitigation.

Evolutionary Applications to Cellular Automata Models for Volcano Risk Mitigation

A GPGPU accelerated evolutionary computation-based decision support system for defining and optimizing volcanic hazard mitigation interventions is proposed. Specifically, the new Cellular Automata numerical model SCIARA-fv3 for simulating lava flows at Mt Etna (Italy) and Parallel Genetic Algorithms (PGA) have been applied for optimizing protective measures construction by morphological evolution. A case study is considered, where PGA are applied for the optimization of the position, orientation and extension of earth barriers built to protect a touristic facility located near the summit of Mt. Etna (Italy) volcano which was interested by the 2001 lava eruption. The methodology has produced extremely positive results and, in our opinion, can be applied within a broader risk assessment framework, having immediate and far reaching implications both in land use and civil defense planning.