LambdaRank has proven effective for optimizing information retrieval metrics such as Normalized Discounted Cumulative Gain (NDCG). However, its application to Precision at document k (P@k) poses significant challenges because of the metric’s unique definition, which heavily restricts the number of effective training document pairs. This limitation diminishes the learning signal for relevant documents beyond the top k, potentially resulting in suboptimal performance. To overcome this, we propose LambdaGap, a ranking algorithm inspired by LambdaRank specifically tailored for optimizing P@k. LambdaGap replaces the pairwise weighting scheme in LambdaRank by one where pairs of documents within k positions in the ranking are masked out. We establish a theoretical link between LambdaGap and P@k by identifying the implicit metric optimized by the model. Furthermore, we introduce a new metric, Average Relevance Position beyond document k, which can be used in conjunction with LambdaRank to indirectly optimize for P@k. Our extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed methods, yielding statistically significant improvements in P@k performance and highlighting their potential for more efficient training.

Every year, forest fires cause damages to biodiversity, atmosphere, and economy. To face such permanent threat, wildfire analysts rely on emerging and well established technologies to determine fire behavior and propagation patterns. Nevertheless, input data describing fire scenarios are subject to high levels of uncertainty that represent a serious challenge for the correctness of the prediction. The unknown parameters need to be adjusted, and an input data calibration phase is carried over following a genetic algorithm strategy. The calibrated input is then pipelined into the actual prediction phase. In addition, this two-stage prediction scheme is leveraged by the cloud computing, which enables high level of parallelism on demand, almost real-time elasticity and unlimited scalability. All of them at a low-cost strategy. In this paper, to obtain more accurate prediction results and efficient use of cloud resources in the compute-intensive calibration phase, we propose a new fitness function in tandem with a strict deadline policy that decreases overall processing time. In consonance with the hard-deadline-driven nature of fire extinction activities, the proposed strategies improve the genetic algorithm convergence and decrease the response time for the calibration stage, setting up an important upper bound limit to the critical compute-intensive adjustment phase. For the case study evaluated, only 3.87% of of accuracy loss is given out in exchange of a guarantee that the calibration phase will never last more than 50 minutes in the worst case.

Forest fires severely affect many ecosystems every year, leading to large environmental damages, casualties, and economic losses. Emerging and established technologies are used to help wildfire analysts determine fire behavior and spread, aiming at more accurate prediction results and efficient use of resources in fire fighting. We propose a novel forest fire spread prediction platform based on a proven two-stage prediction model devised to deal with input data uncertainties. The model is able to calibrate the unknown parameters based on the real observed data using an iterative process. Since this calibration is compute-intensive and due to the unpredictability of urgent computing needs, we exploit an elastic and scalable cloud-based solution platform implemented through coarse-grain parallel processing using a work queue.

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.

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.

Summary 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. © 2016 The Authors.Concurrency and Computation: Practice and ExperiencePublished by John Wiley & Sons Ltd.

This work presents a new algorithm, called Heterogeneous Dynamic Pipeline Mapping, that allows for dynamically improving the performance of pipeline applications running on heterogeneous systems. It is aimed at balancing the application load by determining the best replication (of slow stages) and gathering (of fast stages) combination taking into account processors computation and communication capacities. In addition, the algorithm has been designed with the requirement of keeping complexity low to allow its usage in a dynamic tuning tool. For this reason, it uses an analytical performance model of pipeline applications that addresses hardware heterogeneity and which depends on parameters that can be known in advance or measured at run-time. A wide experimentation is presented, including the comparison with the optimal brute force algorithm, a general comparison with the Binary Search Closest algorithm, and an application example with the Ferret pipeline included in the PARSEC benchmark suite. Results, matching those of the best existing algorithms, show significant performance improvements with lower complexity ( $$O(N^3$$ ), where N is the number of pipeline stages).

Wind field calculation is a critical issue in reaching accurate forest fire propagation predictions. However, when the involved terrain map is large, the amount of memory and the execution time can prevent them from being useful in an operational environment. Wind field calculation involves sparse matrices that are usually stored in CSR storage format. This storage format can cause sparse matrix-vector multiplications to create a bottleneck due to the number of cache misses involved. Moreover, the matrices involved are extremely sparse and follow a very well-defined pattern. Therefore, a new storage system has been designed to reduce memory requirements and cache misses in this particular sparse matrix-vector multiplication. Sparse matrix-vector multiplication has been implemented using this new storage format and taking advantage of the inherent parallelism of the operation. The new method has been implemented in OpenMP, MPI and CUDA and has been tested on different hardware configurations. The results are very promising and the execution time and memory requirements are significantly reduced.

Every year wildfires are responsible of the loss of thousands of hactares of European forest, millions of dollars in damage and, in the worst case, for loss of human lives. Accurate observation of wildland fire evolution is a crucial issue to estimate the burned area, to use the observed data in propagation prediction and to calibrate input parameters of propagation models. In this direction the European Forest Fire Information System (EFFIS) supports the services in charge of the protection of European forests. Satellite images provide useful information, but, in many cases, the clouds or the fire smoke itself do not allow to have a good estimation of the fire front position. So, a methodology based on thermal anomalies information has been applied to estimate fire perimeters even on adverse conditions. This methodology has been recently added to EFFIS to determine the perimeter evolution of a wildfire using thermal anomalies information coming from NASA's satellites. In this paper we describe the algorithm to perform this task and how it was implemented and integrated into the EFFIS services.

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.

• 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 10 5 to 10 8 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.

SUMMARY 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.

In our research work, we use two Dynamic Data Driven Application System (DDDAS) methodologies to predict wildfire propagation. Our goal is to build a system that dynamically adapts to constant changes in environmental conditions when a hazard occurs and under strict real-time deadlines. For this purpose, we are on the way of building a parallel wildfire prediction method, which is able to assimilate real-time data to be injected in the prediction process at execution time. In this paper, we propose a strategy for data injection in distributed environments.

The need for input parameter optimisation in environmental modelling is a long-known and very time-consuming task. However, to avoid tragedy, disaster propagation predictions have to satisfy hard real-time constraints. Especially small disaster control centres with limited computing resources require fast and efficient calibration methods to deliver reliable predictions in time. The combination of a clustering method together with a Genetic Algorithm is used as parameter optimisation technique in forest fire spread prediction. We formalise and demonstrate the potential of the resulting Evolutionary Intelligent System's architecture to solve the complex problem of input parameter calibration on restricted simulation conditions.

Defining performance models associated with the application structure has been proven a useful strategy for implementing dynamic tuning tools. However, for extending this strategy to more complex applications (those composed by different structures) it must integrate a policy for the distribution of the resources among the different application components. Consequently, we propose to take advantage of the knowledge of these models and combine them with a resource management policy for obtaining a global model. In this sense, this work constitutes the ongoing effort in the development of performance models for dynamic tuning.

Performance is a main issue in parallel application development. Dynamic tuning is a technique that acts over application parameters to raise execution performance indexes. To perform that, it is necessary to collect measurements, analyze application behavior using a performance model and carry out tuning actions. Computational Grids present proclivity for dynamic changes on their features during application execution. Thus, dynamic tuning tools are indispensable to reach the expected performance indexes on those environments. A particular problem which provokes performance bottlenecks is the load distribution in master/worker applications. This paper addresses the performance modeling of such applications on Computational Grids for the perspective of dynamic tuning. It is inferred that grain size and number of workers are critical parameters to reduce execution time while raising the efficiency of resources usage. A heuristic to dynamically tune granularity and number of workers is proposed. The experimental simulated results of a matrix multiplication application in a heterogeneous Grid environment are shown.

Abstract 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 © 2006 John Wiley & Sons, Ltd.

article Free Access Share on Teaching parallel processing: development of curriculum and software tools Authors: Jan Kwiatkowski Technical University of Wroclaw, Computer Science Department, Poland Technical University of Wroclaw, Computer Science Department, PolandView Profile , Marek Andruszkiewicz Technical University of Wroclaw, Computer Science Department, Poland Technical University of Wroclaw, Computer Science Department, PolandView Profile , Emilio Luque Universitat Autonoma de Barcelona, Computer Science Department, Spain Universitat Autonoma de Barcelona, Computer Science Department, SpainView Profile , Tomas Margalef Universitat Autonoma de Barcelona, Computer Science Department, Spain Universitat Autonoma de Barcelona, Computer Science Department, SpainView Profile , Jose Cunha Universidade Nova de Lisboa, Departamento de Informatica, Portugal Universidade Nova de Lisboa, Departamento de Informatica, PortugalView Profile , Joao Lourenco Universidade Nova de Lisboa, Departamento de Informatica, Portugal Universidade Nova de Lisboa, Departamento de Informatica, PortugalView Profile , Henryk Krawczyk Technical University of Gdansk Electronics Faculty, Poland Technical University of Gdansk Electronics Faculty, PolandView Profile , Stanislaw Szejko Technical University of Gdansk Electronics Faculty, Poland Technical University of Gdansk Electronics Faculty, PolandView Profile Authors Info & Claims ACM SIGCSE BulletinVolume 28Issue SI1996 pp 159–161https://doi.org/10.1145/237477.237633Published:01 January 1996Publication History 4citation199DownloadsMetricsTotal Citations4Total Downloads199Last 12 Months15Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF