Tomàs Margalef

Euro-Par 2008 – Parallel Processing

This book constitutes the refereed proceedings of the 14th International Conference on Parallel Computing, Euro-Par 2008, held in Las Palmas de Gran Canaria, Spain, in August 2008. The 86 revised papers presented were carefully reviewed and selected from 264 submissions. The papers are organized in topical sections on support tools and environments; performance prediction and evaluation; scheduling and load balancing; high performance architectures and compilers; parallel and distributed databases; grid and cluster computing; peer-to-peer computing; distributed systems and algorithms; parallel and distributed programming; parallel numerical algorithms; distributed and high-performance multimedia; theory and algorithms for parallel computation; and high performance networks.

A distributed diffusion method for dynamic load balancing on parallel computers

Parallel applications can be divided into tasks that can be executed simultaneously in different processors. Depending on prior knowledge about computational requirements of the problem, the assignment of tasks to processors can be guided in two ways: static and dynamic. We propose a new dynamic load balancing algorithm based on the diffusion approach which employs overlapping balancing domains to achieve global balancing. Since current diffusion methods consider discrete units, the algorithms may produce solutions which, although they are locally balanced prove to be globally unbalanced. Our method solves this problem taking into account the load maximum difference between two processors within each domain, providing a more efficient load balancing process.<<ETX>>

Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction

This work represents the first step toward a DDDAS for Wildland Fire Prediction where our main efforts are oriented to take advantage of the computing power provided by High Performance Computing systems to, on the one hand, propose computational data driven steering strategies to overcome input data uncertainty and, on the other hand, to reduce the execution time of the whole prediction process in order to be reliable during real-time crisis. In particular, this work is focused on the description of a Dynamic Data Driven Genetic Algorithm used as steering strategy to automatic adjust certain input data values of forest fire simulators taking into account the underlying propagation model and the real fire behavior.

Design and implementation of a dynamic tuning environment

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.

Injecting Dynamic Real-Time Data into a DDDAS for Forest Fire Behavior Prediction

This work presents a novel idea for forest fire prediction, based on Dynamic Data Driven Application Systems. We developed a system capable of assimilating data at execution time, and conduct simulation according to those measurements. We used a conventional simulator, and created a methodology capable of removing parameter uncertainty. To test this methodology, several experiments were performed based on southern California fires.

MATE: Monitoring, Analysis and Tuning Environment for parallel/distributed applications

The main goal of parallel/distributed applications is to solve the considered problem as fast as possible using the available resources. In this context, the application performance becomes a crucial issue. Developers of these applications must optimize them if they are to fulfill the promise of high‐performance computation. To improve performance, developers search for bottlenecks by analyzing application behavior, try to identify performance problems, determine their causes and overcome them by changing the source code of the application. Current approaches require developers to do these tasks manually and imply a high degree of expertise. Therefore, another approach is needed to help developers during the optimization process. This paper presents the dynamic tuning approach that addresses these issues. In this approach, many tasks are automated and the user intervention and required experience may be significantly reduced. An application is monitored, its performance bottlenecks are detected and it is modified automatically during execution, without recompiling or re‐running it. The introduced modifications adapt the application behavior to changing conditions. We present an environment called MATE (Monitoring, Analysis and Tuning Environment) that has been developed to provide dynamic tuning of parallel/distributed applications. We also show practical experiments conducted with MATE to prove its effectiveness and profitability. Copyright

MATE: Dynamic Performance Tuning Environment

Performance is a key issue in the development of parallel/distributed applications. The main goal of these applications is to solve the considered problem as fast as possible utilizing a certain minimum of parallel system capacities. Therefore, developers must optimize these applications if they are to fulfill the promise of high performance computation. To improve performance, programmers search for bottlenecks by analyzing application behavior, finding problems and solving them by changing the source code. These tasks are especially difficult for non-expert programmers. Current approaches require developers to perform optimizations manually and to have a high degree of experience. Moreover, applications may be executed in dynamic environments. Therefore, it is necessary to provide tools that automatically carry out the optimization process by adapting application execution to changing conditions. This paper presents the dynamic tuning approach that addresses these issues. We also describe an environment called MATE (Monitoring, Analysis and Tuning Environment), which provides dynamic tuning of applications.

S 2 F 2 M : statistical system for forest fire management

One of the most serious problems in wildland fire simulators is the lack of precision for input parameters (moisture content, wind speed, wind direction, etc.). In this paper, a statistical method based on a factorial experiment is presented. This method evaluates a high number of parameter combinations instead of considering a single value for each parameter, in order to obtain a prediction which is closer to reality. The proposed methodology has been implemented in a parallel scheme and tested in a Linux cluster using MPI.

Knowledge-guided Genetic Algorithm for input parameter optimisation in environmental modelling

Abstract 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.

Improved prediction methods for wildfires using high performance computing: a comparison

Recently, dry and hot seasons have seriously increased the risk of forest fire in the Mediterranean area. Wildland simulators, used to predict fire behavior, can give erroneous forecasts due to lack of precision for certain dynamic input parameters. Developing methods to avoid such parameter problems can improve significantly the fire behavior prediction. In this paper, two methods are evaluated, involving statistical and uncertainty schemes. In each one, the number of simulations that must be carried out is enormous and it is necessary to apply high-performance computing techniques to make the methodology feasible. These techniques have been implemented in parallel schemes and tested in Linux cluster using MPI.