Nowadays, the study of high-performance computing (HPC) is one of the essential aspects of postgraduate pro-grammes in Computational Science. However, university education in HPC often suffers from a significant gap b...
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Nowadays, the study of high-performance computing (HPC) is one of the essential aspects of postgraduate pro-grammes in Computational Science. However, university education in HPC often suffers from a significant gap between theoretical concepts and the practical experience of students. To face this challenge, we have implemented an innovative teaching strategy to provide students appropriate resources to ease the assimilation of theoretical con-cepts, while improving their practical experience through the use of teaching tools and resources specifically designed to promote active learning. We have used the proposed strategy to organize the module of Parallel computers and architectures of the Master's in High-Performance Computing, at the Universitat Aut‘onoma de Barcelona, obtaining very promising results. In particular, we have observed improvements of both the academic marks of students and the perception about their own expertise and skills in HPC, regarding the previous teaching approach.
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 in...
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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.
Computational simulation has been used as a powerful tool to represent the dynamical behavior of systems based on complex analytic models. These types of models have two main drawbacks: (a) limitations due to the degr...
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Computational simulation has been used as a powerful tool to represent the dynamical behavior of systems based on complex analytic models. These types of models have two main drawbacks: (a) limitations due to the degree of abstraction needed to simulate them, (b) high computing power to simulate a heavily simplified models. The computing power available today can overcome these limitations to perform quicker simulations of complex models that are closer to reality. In this paper, the experiments and performance analysis of a distributed simulation for a complex individual oriented model (fish schools) are presented. The development of the fish school simulator includes the possibility of working with large models that include large numbers of fish (>10 6 of individuals), predators and obstacles in the simulated world.
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 methodolo...
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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.
The European electrical transmission network is operated increasingly close to its operational limits due to market integration and increased feed-in by renewable energies. For this reason, innovative solutions for a ...
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The potential power provided and possibilities presented by computation graphs has steered most of the available modeling techniques to re-implementing, utilization and including the complex nature of System Biology (...
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The increasing application of network models to translate and analysis of biological systems discusses the necessity of novel methodological and informatics insights for dealing with biological complexity. Today, usin...
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We have applied the Dynamic Data Driven Application System (DDDAS) methodology to predict wildfire propagation. Our goal is to build a system that dynamically adapts to sudden changes in environmental conditions. For ...
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We have applied the Dynamic Data Driven Application System (DDDAS) methodology to predict wildfire propagation. Our goal is to build a system that dynamically adapts to sudden changes in environmental conditions. For this purpose, we are building a parallel wildfire prediction method, which is able to assimilate real-time data to be injected in the prediction process at execution time. This data-injection needs to be intelligent in order noy to disturb the simulation process outputs. In this paper, we propose a policy for data insertion using a statistical approach and we design a set of experiments based on California wildfire where Santa Ana winds generate the ideal conditions for sudden changes in fire behavior.
Exploring the multi-core architecture is an important issue to obtaining high performance in parallel and distributed discrete-event simulations. However, the simulation features must fit on parallel programming model...
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Exploring the multi-core architecture is an important issue to obtaining high performance in parallel and distributed discrete-event simulations. However, the simulation features must fit on parallel programming model in order to increase the performance. In this paper we show our experience developing a hybrid MPI+OpenMP version of our parallel and distributed discrete- event individual-oriented fish schooling simulator. In the hybrid approach developed, we fit our simulation features in the following manner: the communication between the Logical Processes happens via message passing whereas the computing of the individuals by OpenMP threads. In addition, we propose a new data structure for partitioning the fish clusters which avoid the critical section in OpenMP code. As a result, the hybrid version significantly improves the total execution time for huge quantity of individuals, because it decreases both the communication and management of processes overhead, whereas it increases the utilization of cores with sharing of resources.
Parallel Virtual Machine (PVM) and Message Passing Interface (MPI) are the most frequently used tools for programming according to the message passing paradigm, which is considered one of the best ways to develop para...
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ISBN:
(数字)9783540481584
ISBN:
(纸本)9783540665496
Parallel Virtual Machine (PVM) and Message Passing Interface (MPI) are the most frequently used tools for programming according to the message passing paradigm, which is considered one of the best ways to develop parallel applications. This volume comprises 67 revised contributions presented at the Sixth European PVM/MPI Users' Group Meeting, which was held in Barcelona, Spain, 26-29 September 1999. The conference was organized by the computer Science department of the Universitat Autònoma de Barcelona. This conference has been previously held in Liverpool, UK (1998) and Cracow, Poland (1997). The first three conferences were devoted to PVM and were held at the TU Munich, Germany (1996), ENS Lyon, France (1995), and University of Rome (1994). This conference has become a forum for users and developers of PVM, MPI, and other message passing environments. Interaction between those groups has proved to be very useful for developing new ideas in parallel computing and for applying some of those already existent to new practical fields.
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