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This article is an expanded version from the 2020 International Workshop on Integrated Nonlinear Microwave and Millimeter-Wave Circuits, Cardiff, 16-17 July 2020.; International audience; This article aims to discuss challenges and performances of a passive load-pull test bench dedicated to the G-band frequencies. The first task of this work is the development of the setup to perform high power generation, impedance generation, and high-power dynamic of measurement. The solution proposed is the association of an external power system of source and detectors, with an integrated synthesizer of impedance. The proposed system requires several analysis steps in order to be validated. On the other hand, special care is dedicated to the calibration of the scalar measurement in the millimeter-wave frequency range. Then, due to the unknown phase at the input and output of the device under test (DUT), scalar measurement can lead to high inaccuracy. Hence, an adaptive calibration is proposed and applied to the measurement of a bipolar transistor at 185 GHz. We highlight the effect of the missing phase information on the accuracy of measurement. Results of the measurement are discussed.

The Developmental Origins of Health and Disease (DOHaD) framework aims to understand how early life exposures shape lifecycle health. To date, no comprehensive list of these exposures and their interactions has been developed, which limits our ability to predict trajectories of risk and resiliency in humans. To address this gap, we developed a model that uses text-mining, machine learning, and natural language processing approaches to automate search, data extraction, and content analysis from DOHaD-related research articles available in PubMed. Our first model captured 2469 articles, which were subsequently categorised into topics based on word frequencies within the titles and abstracts. A manual screening validated 848 of these as relevant, which were used to develop a revised model that finally captured 2098 articles that largely fell under the most prominently researched domains related to our specific DOHaD focus. The articles were clustered according to latent topic extraction, and 23 experts in the field independently labelled the perceived topics. Consensus analysis on this labelling yielded mostly from fair to substantial agreement, which demonstrates that automated models can be developed to successfully retrieve and classify research literature, as a first step to gather evidence related to DOHaD risk and resilience factors that influence later life human health.

International audience; This paper gives an overall picture from BiCMOS technologies up to THz systems integration, which were developed in the European Research project TARANTO. The European high performance BiCMOS technology platforms are presented, which have special advantages for addressing applications in the submillimeter-wave and THz range. The status of the technology process is reviewed and the integration challenges are examined. A detailed discussion on millimeter-wave characterization and modeling is given with emphasis on harmonic distortion analysis, power and noise figure measurements up to 190 GHz and 325 GHz respectively and S-parameter measurements up to 500 GHz. The results of electrical compact models of active (HBTs) and passive components are presented together with benchmark circuit blocks for model verification. BiCMOS-enabled systems and applications with focus on future wireless communication systems and high-speed optical transmission systems up to resulting net data rates of 1.55 Tbit/s are presented.

International audience; This paper describes two fully-integrated transfer switches designed for in-situ reflectometers and Built-In Self-Test applications (BIST) in the 140 to 220 GHz band (G-band). The proposed switches were designed in the STMicroelectronics 55-nm BiCMOS technology using the Single-Shunt and the Double-Shunt topology, respectively. In the 140 to 195 GHz frequency range, the Double-Shunt Transfer Switch shows an isolation between 27 and 46 dB, and an insertion loss between 5 and 7 dB. Compared to the Single-Shunt Transfer Switch, the double shunt switch presents a much better isolation while having a quite comparable insertion loss and area overhead. To the best of our knowledge, the proposed switches are the first fully integrated transfer switches in BiCMOS or CMOS technologies.

Forest fires provoke significant loses from the ecological, social and economical point of view. Furthermore, the climate emergency will also increase the occurrence of such disasters. In this context, forest fire propagation prediction is a key tool to fight against these natural hazards efficiently and mitigate the damages. However, forest fire spread simulators require a set of input parameters that, in many cases, cannot be measured and must be estimated indirectly introducing uncertainty in forest fire propagation predictions. One of such parameters is the wind. It is possible to measure wind using meteorological stations and it is also possible to predict wind using meteorological models such as WRF. However, wind components are highly affected by the terrain topography introducing a large degree of uncertainty in forest fire spread predictions. Therefore, it is necessary to introduce wind field models that estimate wind speed and direction at very high resolution to reduce such uncertainty. Such models are time consuming models that are usually executed under strict time constrains. So, it is critical to minimize the execution time, taking into account the fact that in many cases it is not possible to execute the model on a supercomputer, but must be executed on commodity hardware available on the field or at control centers. This work introduces a new parallelization approach for wind field calculation based on Python multiprocessing to accelerate wind field evaluation. The results show that the new approach reduces execution time using a single personal computer.

Forest fires severally affect many ecosystems every year, leading to large environmental damages, casualties and economic losses. Established and emerging technologies are used to help wildfire analysts determine fire behavior and spread aiming at a more accurate prediction results and efficient use of resources in fire fighting. Natural hazards simulations need to deal with data input uncertainty and their impact on prediction results, usually resorting to compute-intensive calibration techniques. In this paper, we propose a new evaluation technique capable of reducing the overall calibration time by 60% when compared to the current data-driven approaches. This is achieved by means of the proposed adaptive evaluation technique based on a periodic monitoring of the fire spread prediction error \(\epsilon \) estimated by the normalized symmetric difference for each simulation run. Our new strategy avoid wasting too much computing time running unfit individuals thanks to an early adaptive evaluation.

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.

Nowadays, many fields of science and engineering are evolving through the joint contribution of complementary fields. Computer science, and especially High Performance Computing, has become a key factor in the development of many research fields, establishing a new paradigm called computational science. Researchers and professionals from many different fields require knowledge of High Performance Computing, including parallel programming, to develop fruitful and efficient work in their particular field. Therefore, at Universitat Autonoma of Barcelona (Spain), an interdisciplinary Master on “Modeling for Science and Engineering” was started 5 years ago to provide a thorough knowledge of the application of modeling and simulation to graduate students in different fields (Mathematics, Physics, Chemistry, Engineering, Geology, etc.). In this Master’s degree, “Parallel Programming” appears as a compulsory subject because it is a key topic for them. The concepts learned in this subject must be applied to real applications. Therefore, a complementary subject on “Applied Modeling and Simulation” has also been included. It is very important to show the students how to analyze their particular problems, think about them from a computational perspective and consider the related performance issues. So, in this paper, the methodology and the experience in introducing computational thinking, parallel programming and performance engineering in this interdisciplinary Master’s degree are shown. This overall approach has been refined through the Master’s life, leading to excellent academic results and improving the industry and students appraisal of this programme.

Every year, thousands of forest hectares are burned worldwide, causing important consequences on the atmosphere, biodiversity and economy. Proper prediction of the fire evolution allows to manage the fire fighting equipment properly. Therefore, it is crucial to use reliable and fast simulations in order to predict the evolution of the fire. WRF-SFIRE is a wildland fire simulator, which couples a meteorological model called Weather Research and Forecasting Model (WRF) and a forest fire simulator, SFIRE reproducing the interaction between the propagation of the fire and the atmosphere evolution. The mesh resolution used to solve the atmosphere evolution has a deep impact in the prediction of small scale meteorological effects. At the same time, the ability of introducing these small scale meteorological events into the forest fire simulation implies enhancements in the quality of the data that drives the simulation. Therefore, better fire propagation predictions are obtained. However, this improvement can be affected by the instability of the problem to be solved. This paper states an accuracy study changing the mesh resolution when running WRF-SFIRE using as example a real case that took place in Catalonia (northeast of Spain) in 2005.

Many scientific works have focused on developing propagation models that predict forest fire behavior. These models require a precise knowledge of the environment where the fire is taking place. Geographical Information Systems allow us determining and building the different information layers that define the terrain and the fire. These data, along with meteorological information from weather services, enables the simulation based on real conditions. However, fire spread prediction models require a set of input parameters that, in some cases, are difficult to know or even estimate precisely. Therefore, a framework, based on a genetic algorithm calibration stage, was introduced to reduce the uncertainty in the input parameters and improve the accuracy of the predictions. This stage is implemented using a MPI master/worker scheme and an OpenMP parallel version of the fire spread simulator. Additionally, the whole system is run using suitable automatic worker-assignment and core-allocation policies to respect the existing time restrictions, inherent to this real-world problem. This paper details the process of obtaining the necessary input data as well as the parallel evolutionary framework that delivers the final prediction. A real case study is presented to illustrate the way this framework works.

The objective of this paper is to demonstrate that the use of a distributed approach for the design of the inductance of an LC -tank millimeter-wave (mm-wave) voltage-controlled oscillator (VCO) can lead to higher performance. Performance is improved in both frequency tuning range and power consumption thanks to a reduction of the cross-coupled pair size. The direct comparison of VCOs designed in different technologies is not straightforward, since the performance of the varactors and the inductance widely differ for different fabrication technologies. Hence, two VCOs were designed and integrated in the same CMOS 55-nm technology by using the same varactors. A “classical” VCO using an inductor available in the technology and serving as reference, and a distributed-inductance-based VCO using a slow-wave transmission line were considered. A detailed and efficient approach is adopted for the synthesis of slow-wave transmission line-based inductance without using time-consuming EM simulations. The slow-wave VCO permits to limit the overall size linked to a distributed approach while improving the electrical performance. The experimental results on the proof-of-concept demonstrators at a working frequency of 77 GHz allow validating our proposal.

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.

In the last years, forest fire spread simulators have proven to be very promising tools in the fight against these disasters. Due to the necessity to achieve realistic predictions of the fire behavior in a relatively short time, execution time may be reduced. Moreover, several studies have tried to apply the computational power of GPUs (Graphic Processors Units) to accelerate the simulation of the propagation of fires. Most of these studies use forest fires simulators based on Cellular Automata (CA). CA approaches are fast and its parallelization is relatively easy; conversely, they suffer from precision lack. Elliptical wave propagation is an alternative approach for performing more reliable simulations. Unfortunately, its higher complexity makes their parallelization challenging. Here we explore two different parallel strategies based on Elliptical wave propagation forest fire simulators; the multicore architecture of CPU (Central Processor Unit) and the computational power of GPUs to improve execution times. The aim of this work is to assess the performance of the simulation of the propagation of forest fires on a CPU and a GPU, and finding out when the execution on GPU is more efficient than on CPU. In this study, a fire simulator has been designed based on the basic model for one point evolution in the FARSITE simulator. As study case, a synthetic fire with an initial circular perimeter has been used; the wind, terrain and vegetation conditions have been maintained constant throughout the simulation. Results highlighted that GPUs allow obtaining more accurate results while reducing the execution time of the simulations.

Wind field calculation is a crucial point in predicting forest fire evolution in real emergencies. Such wind field calculation involves complex models that must be solved in a very short time to accomplish the real-time constraints of forest fire propagation prediction. However, when the terrain map under consideration is large (for example, 30 km × 30 km) and the resolution is high (for example, 30 m), the time incurred by wind field calculation makes its use unfeasible in real emergencies. To accelerate the wind field calculation, several approaches have been studied (map partitioning, domain decomposition and matrix storage). The results are quite significant, but in large cases, the execution time does not meet the expected objectives. Therefore, a new approach, integrating all the previous work, has been designed, and a hybrid MPI-OpenMP version of WindNinja has been implemented. This hybrid application exploits different sources of parallelism to reduce execution time without introducing a significant loss of accuracy in wind field calculation. The results obtained with different terrain maps show that the execution time is reduced to below an established limit of 100 s.

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 105 to 108 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.

The accurate prediction of forest fire propagation is a crucial issue to minimize its effects. So, several models have been developed to determine the forest fire propagation beforehand. Such models require several input parameters that, in some cases, cannot be known precisely in a real emergency. So, a Two-Stage methodology was developed to calibrate the input parameters to improve the quality of the prediction. This methodology was based on Genetic Algorithms which require the execution of many simulations. Moreover, when the fire is large some input parameters cannot be considered uniform among the whole fire and extra models must be introduced. One of these non-uniform parameters is wind. So, in this work a wind field model is introduced. This model implies more computation time and response time is the main constraint. The prediction must be provided as fast as possible to be useful, thus it is necessary to exploit all available computing resources. So a Hybrid MPI-OpenMP application has been developed to reach a response in the shortest possible time. This work focuses on reducing the execution time of a worker in a MPI Master/Worker structure analyzing the simulation software parts which compose the Fire Simulator System.

In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate measure of confidence in their performance. There are different ways and different methodologies to establish and measure the confidence of the models. In this paper, we focus on the forest fire spread prediction. Simulators implementing forest fire spread models require diverse input parameters to deliver predictions about fire propagation. However, the data describing the actual scenario where the fire is taking place are usually subject to high levels of uncertainty. In order to minimize the impact of the input-data uncertainty a Two-Stage methodology was developed to calibrate the input parameters in (1) an adjustment stage so that the calibrated parameters are used, and (2) the prediction stage to improve the quality of the predictions. Is in the adjustment stage where the error formula plays a crucial role, because different formulas implies different adjustments and, in consequence, different wild fire spread predictions. In this paper, different error functions are compared to show the impact in terms of prediction quality in DDDAS for forest fire spread prediction. These formulas have been tested using a real forest fire that took place in Arkadia (Greece) in 2011.

Computational fire spread models are a useful tool to mitigate the impact of forest fires. Simulators implementing forest fire spread models require several input parameters to deliver their predictions. Wind speed and direction are sensitive parameters that are usually obtained at low resolution (some kilometers). It is necessary to couple a wind field model, that generates a wind field at high resolution (for example, 30 meters). Even in this case of coupled wind field model-forest fire spread simulator, several parameters include a high degree of uncertainty and usually must be calibrated. One of the strategies consists to introduce a Genetic Algorithm (GA) to calibrate the input parameters according to the actual evolution of the fire. GAs are based on running large amount of simulations, and evaluating and comparing the results provided by each simulation. This fact emphasizes how critical are the metrics used to assess the error of the computational forecasts. The goal of this work is to test eight functions to assess the simulation errors in the case of coupled wind field-forest fire spread prediction to study their drawbacks and advantages to determine which is the most relevant error function.

International audience; Beam-forming techniques using phased arrays are one of the most promising solutions for the practical imple- mentation of future high-data-rate point-to-point communication protocols. The functionality of phased arrays is based on the use of phase shifters that should provide an accurate and controllable phase difference between the different paths of the array. However, the integration of phase shifters in current nanometric technologies is prone to imperfections that may affect the intended phase shift and degrade the performance of the antenna array. This requires extensive testing and calibration and represents a bottleneck in the production line of these system. In this work, we propose a simple Oscillation-Based Test technique that may be suitable for Built-In Self-Test applications of phase shifters. The technique is demonstrated on a Reflection-Type Phase Shifter implemented in a 55 nm BiCMOS technology. Elec- tromagnetic and electrical simulation results show the feasibility of the proposed technique.

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.

We present a technique for automated application performance modeling and analysis.A parallel application is modeled by communication and computational activities.The analysis discovers causal dependencies between problems and infers root causes.It compares concurrent execution flows correlating problems in causal relationship.Results of root-cause analysis for different MPI applications are encouraging. The evolution of hardware is improving at an incredible rate. However, the advances in parallel software have been hampered for many reasons. Developing an efficient parallel application is still not an easy task. Applications rarely achieve good performances immediately and, therefore, careful performance analysis and optimization are crucial. These tasks are difficult and require a thorough understanding of the programs behavior. In this paper, we propose a systematic approach to online root-cause performance analysis. The automated analysis uses an online model to quickly identify the most important performance problems, and correlates them with application source code. Our technique is able to discover causal dependencies among the problems, infer their root causes and explain them to developers. In all of the scenarios we performed, this online modelling and analysis approach allowed us to understand the behavior of the applications, evaluate the performance and locate problem causes without specific knowledge of application internals.

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.

Forest fire propagation prediction is a key point to fight against such hazards. Several models and simulators have been developed to predict forest fire propagation. These models require input parameters such as digital elevation map, vegetation map, and other parameters describing the vegetation and meteorological conditions. Coupling wind field model and forest fire propagation model improves accuracy prediction, but increases significantly prediction time. This fact is critical since propagation prediction must be provided in advance to allow the control centers to manage firefighters in the best possible way. This work analyses WindNinja execution time, describes a WindNinja parallelisation based on map partitioning, determines the limitations of such methodology for large maps and presents an improvement based on adapting map resolution to accomplish execution time limitations.

The most important aspect that affects the reliability of environmental simulations is the un- certainty on the parameter settings describing the environmental conditions, which may involve important biases between simulation and reality. To relieve such arbitrariness, a two-stage pre- diction method was developed, based on the adjustment of the input parameters according to the real observed evolution. This method enhances the quality of the predictions, but it is very demanding in terms of time and computational resources needed. In this work, we describe a methodology developed for response time assessment in the case of fire spread prediction, based on evolutionary computation. In addition, a parallelization of one of the most used fire spread simulators, FARSITE, was carried out to take advantage of multicore architectures. This al- lows us to design proper allocation policies that significantly reduce simulation time and reach successful predictions much faster. A multi-platform performance study is reported to analyze the benefits of the methodology.

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.

The Two-Stage forest fire spread prediction methodology was developed to enhance forest fire evolution forecast by tackling the uncertainty of some environmental conditions. However, there are parameters, such as wind, that present a variation along terrain and time. In such cases, it is necessary to couple forest fire propagation models and complementary models, such as meteorological forecast and wind field models. This multi-model approach improves the accuracy of the predictions by introducing an overhead in the execution time. In this paper, different multi-model approaches are discussed and the results show that the propagation prediction is improved. Exploiting multi-core architectures of current processors, we can reduce the overhead introduced by complementary models.

Forests fires are a significant problem especially in countries of the Mediterranean basin. To fight against these disasters, the accurate prediction of forest fire propagation is a crucial issue. Propagation models try to describe the future evolution of the forest fire given an initial scenario and certain input parameters. However, the data describing the real fire scenario are usually subject to high levels of uncertainty. Moreover, there are input parameters that present spatial and temporal variation that make the prediction less accurate. Therefore, to overcome such uncertainty and improve accuracy it is necessary to couple complementary models such as the case of the wind field model. Such models use the meteorological forecasted wind to provide the wind direction and speed depending on the topography of the terrain. We use WindNinja as wind field simulator. This simulator takes a lot of time to deliver the predictions and it is a serious problem because fire propagation prediction must accomplish strict time constraints. To solve this problem, we propose map partitioning and solving independently for each one of the parts. However, the model has problems concerning boundary effects which is an additional source of uncertainty. Therefore, it is necessary to apply certain degree of overlapping among parts to reach a stable wind field without inconsistencies and a minimum uncertainty.

Performance is a main issue in parallel application development. Dynamic tuning is a technique that changes certain applications' parameters on-line to improve their performance adapting the execution to actual conditions. To perform that, it is necessary to collect measurements, analyze application behavior and carry out tuning actions during the application execution. Computational Grids present proclivity for dynamic changes in the environment during the application execution. Therefore, dynamic tuning tools are necessary to reach the expected performance indexes of applications on those environments. This paper addresses the dynamic tuning of parallel/distributed applications on Computational Grids. We analyze Grid environments to determine their characteristics and we present the development of dynamic tuning tool GMATE enabled for such environments. The performance analysis is based on performance models that indicate how to improve the application execution. A particular problem which provokes performance bottlenecks is the load imbalance in Master/Worker applications. A heuristic to dynamically tune granularity of work and number of workers is proposed. Finally, we describe the experimental validation of the performance model and its applicability on a set of real parallel applications.

The exploitation of throughput in a parallel application that processes an input data stream is a difficult challenge. For typical coarse-grain applications, where the computation time of tasks is greater than their communication time, the maximum achievable throughput is determined by the maximum task computation time. Thus, the improvement in throughput above this maximum would eventually require the modification of the source code of the tasks. In this work, we address the improvement of throughput by proposing two task replication methodologies that have the target throughput to be achieved as an input parameter. They proceed by generating a new task graph structure that permits the target throughput to be achieved. The first replication mechanism, named DPRM (Data Parallel Replication Mechanism), exploits the inner task data parallelism. The second mechanism, named TCRM (Task Copy Replication Mechanism), creates new execution paths inside the application task graph structure that allows more than one instance of data to be processed concurrently. We evaluate the effectiveness of these mechanisms with three real applications executed in a cluster system: the MPEG2 video compressor, the IVUS (Intra-Vascular Ultra-Sound) medical image application and the BASIZ (Bright and SAtured Images Zone) video processing application. In all these cases, the obtained throughput was greater after applying the proposed replication mechanism than what the application could provide with the original implementation.

The accurate prediction of forest fire propagation is a crucial issue to minimize its effects. Several models have been developed to determine the forest fire propagation. Simulators implementing such models require diverse input parameters to deliver predictions about fire propagation. However, the data describing the actual scenario where the fire is taking place are usually subject to high levels of uncertainty. The input-data uncertainty represents a serious drawback for the correctness of the prediction. So, a two-stage methodol- ogy was developed to calibrate the input parameters in an adjustment stage so that the calibrated parameters are used in the prediction stage to improve the quality of the predictions. This way, we relieve the effects of such uncertainty. In this work, we take advantage of this two stage methodology applying Genetic Algorithms as the calibration technique. However, the use of Genetic Algorithms require the execution of many simulations. This fact, added to the eventual long executions of the underlying simulator (due to its inherent complexity), implies to deal with another serious problem: the time needed to deliver the predictions. To be useful, the prediction must be provided much faster than real time. So, it is necessary to exploit all available computing resources. In this work, we present a two-stage forest fire spread prediction hybrid MPI-OpenMP application based on the Master- Worker paradigm and the parallelization of the FARSITE simulator in order to minimize the response time. The results as regards the enhancement in the quality of the predictions are reported, as well as the results regarding the time saving obtained by this hybrid application.

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 oftraditionalpredictions, 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.

Wind field calculation is a common problem in different environmental applications from design of wind farms to forest fire propagation prediction. Calculating the wind field is a complex problem that involves solving huge linear systems. Solving such systems requires the use of iterative methods, such as Preconditioned Conjugate Gradient (PCG) that in most cases take long execution time. The PCG solver with different preconditioners has been analyzed and the performance and scalability of this solver has been determined. The most time consuming operations have been identified and a new method has been developed to improve the parallelization reducing the execution time and increasing the scalability. The new method has been applied on a wind field simulator, called WindNinja, usually coupled to forest fire propagation models. The results are very promising and the new parallelization method appears as a key point to be integrated in other approaches.

The use of high performance computing has been gaining more and more followers in the different branches of Science and Engineering given the potential offered to deal with complex and big problems. However, the parallel programming paradigm involves additional aspects to the merely functional, which could provoke different kinds of bottlenecks in the performance of the applications. Such diff culties may represent critical obstacles, especially for the non-expert users. In this work, we consider the limitations of the automatic development of parallel applications, and the obstacles in the performance tuning process, and we propose a built-in environment that integrates these both processes in a transparent manner. We call our proposal as Environment for automatic development and tuning of Parallel Applications (EPA). EPA follows a cooperative approach: (i) It provides an interface to guide the user in the depiction of the pair problem-solution by completing a form; (ii) such information is automatically formalized in a specif cation; (iii) the specif ed information is automatically used by EPA to instantiate an instrumented skeleton; (iv) the skeleton of the parallel application is a priori enriched in EPA with tuning instrumentation, for monitoring, analysis and tuning actions; (v) the tunable parallel application is automatically generated by EPA; (vi) the user is able to execute the application with no more effort than having completed a form. Clearly, EPA makes transparent the process of code generation and instrumentation, which represents an important advantage especially for non-expert users. Given that the parallelization of any pair problem-solution is hard to generalize, the environment proposes to tackle different parallel problem solvers. The tuning knowledge injected in the generated applications is based on performance models, which makes the decision-making process concise. The Environment could include as problem solvers as needed. In this article, we introduce the problem solver constituted by the Parallel Genetic Algorithms, and — with respect to the tuning process — we consider the improvement of the amount of computational resourses involved in the execution.

This work represents the first step towards a Dynamic Data-Driven Application System (DDDAS) for wildland fire prediction. Our main efforts are focused on taking advantage of the computing power provided by High Performance Computing systems and to propose computational data-driven steering strategies to overcome input data uncertainty. In doing so, prediction quality can be enhanced significantly. On the other hand, these proposals reduce the execution time of the overall prediction process in order to be of use during real-time crisis. In particular, this work describes a Dynamic Data-Driven Genetic Algorithm (DDDGA) used as steering strategy to automatically adjust highly dynamic input data values of forest fire simulators taking into account the underlying propagation model and real fire behaviour.

We propose to use knowledge about a parallel application's structure that was acquired with the use of a skeleton based development strategy to dynamically improve its performance. Parallel/distributed programming provides the possibility of solving highly demanding computational problems. However, this type of application requires support tools in all phases of the development cycle because the implementation is extremely difficult, especially for non-expert programmers. This work shows a new strategy for dynamically improving the performance of pipeline applications. We call this approach Dynamic Pipeline Mapping (DPM), and the key idea is to have free computational resources by gathering the pipeline's fastest stages and then using these resources to replicate the slowest stages. We present two versions of this strategy, both with complexity O(Nlog(N)) on the number of pipe stages, and we compare them to an optimal mapping algorithm and to the Binary Search Closest (BSC) algorithm [1]. Our results show that the DPM leads to significant performance improvements, increasing the application throughput up to 40% on average.

The increasing use of parallel/distributed applications demands a continuous support to take significant advantages from parallel power. This includes the evolution of performance analysis and tuning tools which automatically allows for obtaining a better behavior of the applications. Different approaches and tools have been proposed and they are continuously evolving to cover the requirements and expectations of users. One such tool is MATE (Monitoring Analysis and Tuning Environment), which provides automatic and dynamic tuning for parallel/distributed applications. The knowledge used by MATE to analyze and take decisions is based on performance models which include a set of performance parameters and a set of mathematical expressions modeling the solution of the performance problem. These elements are used by the tuning environment to conduct the monitoring and analysis steps, respectively. The tuning phase depends on the results of the performance analysis. This paper presents a methodology to specify performance models. Each performance model specification can be automatically and transparently translated into a piece of software code encapsulating the knowledge to be straightforwardly included in MATE. Applying this methodology, the user does not have to be involved in the implementation details of MATE, which makes the usage of the tool more transparent. Copyright © 2011 John Wiley & Sons, Ltd.

A quick response becomes crucial in natural hazard management. When an emergency occurs, hazard evolution simulators are a very helpful tool for the teams in charge of making decisions. To perform the simulations, they rely on data which usually constitutes a big set of parameters, which have been previously recorded from observations, usually coming from remote sensors, images, etc. However, this data is frequently subject to a high degree of uncertainty. This data uncertainty also produces uncertainty in simulators’ results. To overcome this drawback, in previous works we developed a two-stage prediction method, which has been demonstrated to improve noticeably the quality of the predictions. The time incurred in performing this strategy, however, may vary significantly depending on different factors. As it is well known, the execution time of a particular simulator depends on the specific setting of the input parameters. Moreover, decision control centers in charge of making decisions to fight against the ongoing disaster require a certain degree of quality in the final prediction. Depending on how demanding are the quality and time constraints, the two-stage strategy may need the support of Urgent Computing solutions, in order to meet the requirements. In this work, we focus on forest fires spread prediction, as a real application case of study, and we expose our methodology to characterize both the time needed and the expected quality of the two-stage prediction method, so that we are able to determine the amount of computational resources needed to respond effciently to an eventual emergency. This way, we emphasize the need of Urgent Computing mechanisms to be able to put into practice this method at real time.

Natural hazards are significant problems that every year cause important loses around the world. A good prediction of the behavior of the hazards is a crucial issue to fight against them and to minimize the damages. The models that represent these phenomena need several input parameters and in many cases, such parameters are diffcult to know or even to estimate in a real scenario. So, a methodology based on the DDDAS paradigm was developed to calibrate the input parameters according to real observations of the behavior and evolution of the hazard. Such calibrated parameters are then used to provide an improved prediction for the next time interval. This methodology was tested on Forest Fire Propagation Prediction with significant results. The developed methodology takes the fire behavior and propagation during a time interval and then searches for the values of the input parameters that best reproduce the propagation of the fire during that interval. Several Artificial Intelligence (AI) methods were applied to carry out this search as fast as possible. The values of the parameters that best reproduce the behavior of the fire were then used as input parameters to predict the propagation during the next time interval. These parameters were considered constant during both time intervals and a single value for each parameter was used for the calibrating process and for the prediction stage. This methodology fits on the DDDAS paradigm since the prediction is dynamically driven by the system evolution. However, there are several parameters that are not constant through time, but they may vary dynamically. In the case of forest fires, a typical example is the wind. In some cases, when the time interval is short an average value for the wind can be a feasible value, but when the time interval is longer, in most cases, a single value cannot represent the variability of the wind. We can estimate wind behavior applying some complementary model. In this work, we are going a step further considering the dynamic behavior of such parameters. We propose an extension of the existing prediction scheme that takes into account the dynamically changing parameters by coupling a weather prediction system on a DDDAS Forest Fire Propagation Prediction system.

When an emergency occurs, hazard evolution simulators are a very helpful tool for the teams in charge of making decisions. These simulators need certain input data, which defines the characteristics of the environment where the emergency is taking place. This kind of data usually constitutes a big set of parameters, which have been previously recorded from observations, usually coming from remote sensors, pictures, etc. However, this data is frequently subject to a high degree of uncertainty, as well as the results produced by the corresponding simulators. Hence, it is also necessary to pay attention to the simulations’ quality and reliability. In this work we expose the way we deal with such uncertainty. Our research group has previously developed a two-stage prediction methodology that introduces an adjustment stage in order to deal with the uncertainty on the simulator input parameters. This method significantly improves predictions’ quality, however, in order to be useful, a good characterization of the adjustment techniques has to be carried out so that we are able to choose the best configuration of them, given certain restrictions regarding resources availability and time deadlines. In this work, we focus on forest fires spread prediction as a real study case, for which Genetic Algorithms (GA) have been demonstrated to be a suitable adjustment strategy. We describe the methodology used to characterize the GA and we also validate it when assessing in advance the quality of the fire spread prediction.

Several Data-Driven Methods have been developed to try to solve the input parameters uncertainty when considering problems like Wildfires Prediction. In general, these methods operate over a large number of input parameters, and consider the most recent known behavior of wildfires. The purpose of the methods is to find the parameter set that best describes the real situation under consideration. Therefore, it is presumed that the same set of values could be used to predict the immediate future. However, because this kind of prediction is based on a single set of parameters, for those parameters that present a dynamic behavior (e.g. wind direction and speed), the new optimized values are not adequate to make a prediction. In this paper we propose an alternative method developed in a new branch of Data-Driven Prediction, which we called Multiple Overlapping Solution . This method combines statistical concepts and HPC (High Performance Computing) to obtain a higher quality prediction.

The need for input parameter optimisation in environmental modelling is long known. Real-time constraints of disaster propagation predictions require fast and efficient calibration methods to deliver reliable predictions in time to avoid tragedy. Lately, evolutionary optimisation methods have become popular to solve the input parameter problem of environmental models. Applying a knowledge-guided Genetic Algorithm (GA) we demonstrate how to speed up parameter optimsation and consequently the propagation prediction of environmental disasters. Knowledge, obtained from historical and synthetical disasters, is stored in a knowledge base and provided to the GA in terms of a knowledge chromosome. Despite of increased loads of knowledge, its retrieval times can be kept near-constant. During GA mutation, ranges of selected parameters are limited forcing the GA to explore promising solution areas. Experiments in forest fire spread prediction show how time-consuming fitness evaluations of the GA could be lowered remarkably to cope with real-time capabilities maintaining the error magnitude.

Parallel/distributed systems are continuously growing. This allows and enables the scalability of the applications, either by considering bigger problems in the same period of time or by solving the problem in a shorter time. In consequence, the methodologies, approaches and tools related to parallel paradigm should be brought up to date to support the increasing requirements of the applications and the users. MATE (Monitoring, Analysis and Tuning Environment) provides automatic and dynamic tuning for parallel/distributed applications. The tuning decisions are made according to performance models, which provide a fast means to decide what to improve in the execution. However, MATE presents some bottlenecks as the application grows, due to the fact that the analysis process is made in a full centralized manner. In this work, we propose a new approach to make MATE scalable. In addition, we present the experimental results and the analysis to validate the proposed approach against the original one.

Load imbalance among workers is one of the main causes of performance shortcomings in Master-Worker applications. We have observed that this problem is very similar to the one of scheduling distributed parallel loops, which has been widely in the literature. Thus, we have adapted one of the most successful algorithms, known as Factoring, to be used for Master-Worker applications. This has leads to a simple an elegant strategy that can be used to obtain an excellent automatic and dynamic load balancing strategy for the workers. Finally, we have assessed the resulting strategy through extensive experimentation and simulation.

The main goal of parallel/distributed applications is to solve a considered problem as fast as possible using the minimum amount of system resources. In this context, the application performance becomes a crucial issue and developers of parallel/distributed applications must optimize them to provide high performance computation. Typically, to improve performance, developers analyze the application behavior, search for bottlenecks, determine their causes and change the source code. In this paper, we present the dynamic, automatic tuning approach. This approach aims at automating these tasks and minimizing user intervention. An application is monitored, its performance bottlenecks are detected and it is modified automatically during the execution, without recompiling or re-running it. The modifications introduced adapt the application behavior to the changing conditions. This paper describes design and implementation of the MATE environment (Monitoring, Analysis and Tuning Environment), which we have developed as a step towards dynamically tuning parallel/distributed applications.

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. Solving such systems takes too much time and makes the approach unfeasible in real time operation. To overcome this problem the Schwarz alternating domain decomposition can be applied. 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 and the wind field calculation time can be significantly reduced.

International Conference on Computational Science, ICCS 2015 – Computational Science at the Gates of Nature Forest fire devastate every year thousand of hectares of forest around the world. Fire behavior prediction is a useful tool to aid coordination and management of human and mitigation resources when fighting against these kind of hazards. Any fire spread forecast system requires to be fitted with different kind of data with a high degree of uncertainty, such as for example, me- teorological data and vegetation map among others. The dynamics of this kind of phenomena requires to develop a forecast system with the ability to adapt to changing conditions. In this work two different fire spread forecast systems based on the Dynamic Data Driven Application paradigm are applied and an alternative approach based on the combination of both predictions is presented. This new method uses the computational power provided by high performance computing systems to deliver the predictions under strict real time constraints. This research has been supported by the Ministerio de Economía y Competitividad (MECSpain) under contract TIN2011-28689-C02-01 and the Catalan government under grant 2014- SGR-576.

Predicting the potential danger of a forest fire is an essential task of wildfire analysts. For that reason, many scientists have focused their efforts on developing propagation models that predict forest fire evolution to mitigate the consequences of such hazards. These propagation models require a precise knowledge of the whole environment where the fire is taking place. In the context of natural hazards simulation, it is well known that, part of the final forecast error comes from the uncertainty in the input data. In this work, we use a Dynamic Data-driven methodology to overcome such problem. The core of the methodology is a calibration stage previous to the forecast where complementary models, data injection and intelligent systems are working in a symbiotic way to reduce the forecast errors at real time. This approach has been tested using a forest fire that took place in Arkadia (Greece) in 2011.

One of the challenges still open to wildland fire simulators is the capacity of working under real-time constrains with the aim of providing fire spread predictions that could be useful in fire mitigation interventions. We propose going one step beyond the classical wildland fire prediction by linking evolutionary optimization strategies to the traditional scheme with the aim of emulating an "ideal" fire propagation model as much as possible. In order to accelerate the fire prediction, this enhanced prediction scheme has been developed in a fashion on a Linux cluster using MPI. Furthermore, a sensitivity analysis has been carried out to determine the input parameters that we can fix to their typical values in order to reduce the search-space involved in the optimization process and, therefore, accelerates the whole prediction strategy.