Estudo do consumo energético e de ar comprimido de um sistema pneumático de posicionamento

O objetivo do trabalho desenvolvido é obter um método de cálculo para estimar o consumo de energia e de ar comprimido de um sistema pneumático acionado com controle de posicionamento em malha fechada. Para tanto, é realizada a modelagem física e matemática do consumo de energia de um atuador pneumático, considerando, entre outras variáveis, as pressões, temperaturas, e vazões de ar comprimido nas suas câmaras. Após, são realizados testes práticos com um sistema acionado por um módulo de controle utilizando a posição do pistão como sinal de realimentação. São adquiridos, também, os sinais de pressão das câmaras, de modo a poder calcular o consumo de energia do sistema montado em bancada. Por fim, os resultados são analisados criticamente, comparando os dados de consumo energético e de ar comprimido calculados através das experiências com os modelos do sistema, realizando ajustes e correções, e justificando as eventuais discrepâncias. Os resultados encontrados para o rendimento do sistema pneumático estão de acordo com os valores encontrados na bibliografia pesquisada.The goal of this report is to develop a method to estimate the energy and air consumption of a pneumatic system driven by a closed-loop positioning control module. In order to do so, the physical and mathematical modeling of the energy consumption of a pneumatic actuator will be presented, taking into consideration the pressure, temperature, and flow of compressed air in its chambers, among other variables. Then, practical experiments are made with a system driven by a control module, using the position of the piston as a feedback signal. Air pressure on each chamber is also monitored, in order to be able to calculate the energy consumption of the system. Finally, the results are critically analysed, comparing the energy and air consumption calculated to the system models, making adjustments and corrections, and justifying any discrepancies. The results regarding the energy efficiency of the system agree with those found in the literature

Evaluating Trace Aggregation Through Entropy Measures for Optimal Performance Visualization of Large Distributed Systems

Large-scale distributed high-performance applications are involving an ever-increasing number of threads to explore the extreme concurrency of today's systems. The performance analysis through visualization techniques usually su ers severe semantic limitations due, from one side, to the size of parallel applications, from another side, to the challenges to visualize large-scale traces. Most of performance visualization tools rely therefore on data aggregation in order to be able to scale. Even if this technique is frequently used, to the best of our knowledge, there has not been any real attempt to evaluate the quality of aggregated data for visualization. This paper presents an approach which lls this gap. We propose to build optimized macroscopic visualizations using measures inherited from information theory, and in particular the Kullback-Leibler divergence. These measures are used to estimate the complexity reduced and the information lost during any given data aggregation. We rst illustrate the applicability of our approach by exploiting these two measures in the analysis of work stealing traces using squari ed treemaps. We then report the e ective scalability of our approach by visualizing known anomalies in a synthetic trace le with the behavior of one million processes, with encouraging results

Multi-Phase Task-Based HPC Applications: Quickly Learning how to Run Fast

International audienceParallel applications performance strongly depends on the number of resources. Although adding new nodes usually reduces execution time, excessive amounts are often detrimental as they incur substantial communication overhead, which is difficult to anticipate. Characteristics like network contention, data distribution methods, synchronizations, and how communications and computations overlap generally impact the performance. Finding the correct number of resources can thus be particularly tricky for multi-phase applications as each phase may have very different needs, and the popularization of hybrid (CPU+GPU) machines and heterogeneous partitions makes it even more difficult. In this paper, we study and propose, in the context of a task-based GeoStatistic application, strategies for the application to actively learn and adapt to the best set of heterogeneous nodes it has access to. We propose strategies that use the Gaussian Process method with trends, bound mechanisms for reducing the search space, and heterogeneous behavior modeling. We compare these methods with traditional exploration strategies in 16 different machines scenarios. In the end, the proposed strategies are able to gain up to ≈51% compared to the standard case of using all the nodes while having low overhead

A Trace Macroscopic Description based on Time Aggregation

Trace visualization; trace analysis; trace overview; time aggregation; parallel systems; embedded systems; information theory; scientific computation; multimedia application; debugging; optimizationToday, because of computing system complexity, it is required to trace application executions to understand their behavior. Visualization techniques provide some help in representing their content, but their scalability is limited both because of human perception and bounded screen resolution. To solve this issue, we propose a visualization based on time aggregation that provides a concise overview of a trace whatever its size. The level of details in this visualization can be configurable by users who can adjust the compromise between concision (gain from aggregation) and information loss. They can then refine their analysis by zooming in an interesting part and choosing a less aggregated overview for this interesting part. This visualization is implemented in our tool, Ocelotl, which enables users to interact with this visualization by changing the selected time interval and its aggregation settings dynamically. The results presented in this paper show that the technique can help users correctly identify anomalies in very large trace files composed of up to forty million events.De nos jours, à cause de la complexité des systèmes actuels, les analystes utilisent le traçage pour comprendre le comportement des programmes. Les techniques de visualisation aident à représenter le contenu de ces traces, mais le passage à l'échelle est limité par la perception humaine des données affichées ainsi que par la résolution des écrans. Dans le but de résoudre ce problème, nous proposons une technique de visualisation faisant appel à une algorithme d'agrégation, fournissant un aperçu du contenu de la trace quelle que soit sa taille. Le niveau de détail peut être ajusté par l'utilisateur, grâce à un compromis entre la réduction de complexité de la représentation (gain dû à l'agrégation) et la perte d'information. L'utilisateur peut ensuite raffiner l'analyse en zoomant sur des parties intéressantes de la trace et en diminuant l'intensité de l'agrégation. Cette technique est implémentée dans notre outil, Ocelotl, qui permet à l'utilisateur d'interagir avec la visualisation en changeant les bornes de temps et les paramètres de l'agrégation de manière dynamique. Les résultats présentés dans ce rapport montrent que notre contribution aide les utilisateurs à identifier des anomalies dans des traces contenant jusqu'à quarante millions d'événements

Communication-Aware Load Balancing of the LU Factorization over Heterogeneous Clusters

International audienceLarge clusters and supercomputers are rapidly evolving and may be subject to regular hardware updates that increase the chances of becoming heterogeneous. Homogeneous clusters may also have variable performance capabilities due to processor manufacturing, or even partitions equipped with different types of accelerators. Data distribution over heterogeneous nodes is very challenging but essential to exploit all resources efficiently. In this article, we build upon task-based runtimes' flexibility to study the interplay between static communication-aware data distribution strategies and dynamic scheduling of the linear algebra LU factorization over heterogeneous sets of hybrid nodes. We propose two techniques derived from the state-of-the-art 1D×1D data distributions. First, to use fewer computing nodes towards the end to better match performance bounds and save computing power. Second, to carefully move a few blocks between nodes to optimize even further the load balancing among nodes. We also demonstrate how 1D×1D data distributions, tailored for heterogeneous nodes, can scale better with homogeneous clusters than classical block-cyclic distributions. Validation is carried out both in real and in simulated environments under homogeneous and heterogeneous platforms, demonstrating compelling performance improvements

The complex gas kinematics in the nucleus of the Seyfert 2 galaxy NGC 1386: rotation, outflows and inflows

We present optical integral field spectroscopy of the circum-nuclear gas of the Seyfert 2 galaxy NGC 1386. The data cover the central 7$^{\prime\prime} \times 9^{\prime\prime}$ (530 $\times$ 680 pc) at a spatial resolution of 0.9" (68 pc), and the spectral range 5700-7000 \AA\ at a resolution of 66 km s$^{-1}$. The line emission is dominated by a bright central component, with two lobes extending $\approx$ 3$^{\prime\prime}$ north and south of the nucleus. We identify three main kinematic components. The first has low velocity dispersion ($\bar \sigma \approx$ 90 km s$^{-1}$), extends over the whole field-of-view, and has a velocity field consistent with gas rotating in the galaxy disk. We interpret the lobes as resulting from photoionization of disk gas in regions where the AGN radiation cones intercept the disk. The second has higher velocity dispersion ($\bar \sigma \approx$ 200 km s$^{-1}$) and is observed in the inner 150 pc around the continuum peak. This component is double peaked, with redshifted and blueshifted components separated by $\approx$ 500 km s$^{-1}$. Together with previous HST imaging, these features suggest the presence of a bipolar outflow for which we estimate a mass outflow rate of $\mathrm{\dot M} \gtrsim$ 0.1 M$_{\odot}$ yr$^{-1}$. The third component is revealed by velocity residuals associated with enhanced velocity dispersion and suggests that outflow and/or rotation is occurring approximately in the equatorial plane of the torus. A second system of velocity residuals may indicate the presence of streaming motions along dusty spirals in the disk.Comment: 24 pages, 16 figures, 3 tables, interesting results, accepted for publication in Ap

Ocelotl: Large Trace Overviews Based on Multidimensional Data Aggregation

International audiencePerformance analysis of parallel applications is commonly based on execution traces that might be investigated through visualization techniques. The weak scalability of such techniques appears when traces get larger both in time (many events registered) and space (many processing elements), a very common situation for current large-scale HPC applications. In this paper we present an approach to tackle such scenarios in order to give a correct overview of the behavior registered in very large traces. Two configurable and controlled aggregation-based techniques are presented: one based exclusively on the temporal aggregation, and another that consists in a spatiotemporal aggregation algorithm. The paper also details the implementation and evaluation of these techniques in Ocelotl, a performance analysis and visualization tool that overcomes the current graphical and interpretation limitations by providing a concise overview registered on traces. The experimental results show that Ocelotl helps in detecting quickly and accurately anomalies in 8 GB traces containing up to two hundred million of events

A Spatiotemporal Data Aggregation Technique for Performance Analysis of Large-scale Execution Traces

International audienceAnalysts commonly use execution traces collected at runtime to understand the behavior of an application running on distributed and parallel systems. These traces are inspected post mortem using various visualization techniques that, however, do not scale properly for a large number of events. This issue, mainly due to human perception limitations, is also the result of bounded screen resolutions preventing the proper drawing of many graphical objects. This paper proposes a new visualization technique overcoming such limitations by providing a concise overview of the trace behavior as the result of a spatiotemporal data aggregation process. The experimental results show that this approach can help the quick and accurate detection of anomalies in traces containing up to two hundred million events

Performance Analysis of Irregular Task-Based Applications on Hybrid Platforms: Structure Matters

International audienceEfficiently exploiting computational resources in heterogeneous platforms is a real challenge which has motivated the adoption of the task-based programming paradigm where resource usage is dynamic and adaptive. Unfortunately, classical performance visualization techniques used in routine performance analysis often fail to provide any insight in this new context, especially when the application structure is irregular. In this paper, we propose several performance visualization techniques and modeling strategies motivated by the analysis of task-based multifrontal sparse linear solvers whose structure is particularly complex. We show that by building on both a performance model of irregular tasks and on structure of the application (in particular the elimination tree), we can detect and highlight anomalies and understand resource utilization from the application point-of-view in a very insightful way. We validate these novel performance analysis techniques with the QR_mumps sparse parallel solver by describing a series of case studies where we identify and address non trivial performance issues thanks to our visualization methodology

Interactive Analysis of Large Distributed Systems with Topology-based Visualization

The performance of parallel and distributed applications is highly dependent on the characteristics of the execution environment. In such environments, the network topology and characteristics directly impact data locality and movements as well as contention, which are key phenomena to understand the behavior of such applications and possibly improve it. Unfortunately few visualization available to the analyst are capable of accounting for such phenomena. In this paper, we propose an interactive topology-based visualization technique based on data aggregation that enables to correlate network characteristics, such as bandwidth and topology, with application performance traces. We claim that such kind of visualization enables to explore and understand non trivial behavior that are impossible to grasp with classical visualization techniques. We also claim that the combination of multi-scale aggregation and dynamic graph layout allows our visualization technique to scale seamlessly to large distributed systems. We support these claims through a detailed analysis of a high performance computing scenario and of a grid computing scenario.Les performances des applications parallèles et distribuées dépendent fortement des caractéristiques de l'environnement d'exécution. Dans de tels environnements, la topologie du réseau et ses caractéristiques ont un impact direct sur la localité et les mouvements des données ainsi que sur la contention, qui sont des phénomènes clés pour comprendre le comportement de ces applications et éventuellement les améliorer. Malheureusement, peu de visualisation permettent de mettre en évidence ces phénomènes. Dans cet article, nous proposons une technique de visualisation interactive et topologique basée sur l'agrégation de données qui permet de corréler les caractéristiques du réseau, telles que la bande passante et la topologie, avec des traces de performances des applications. Ce type de visualisation permet d'explorer et de comprendre des comportements non triviaux qui sont impossibles à appréhender avec les techniques de visualisation classiques. Nous affirmons également que la combinaison de l'agrégation multi-échelle et l'agencement dynamique du graphe permet à notre technique de visualisation de passer à l'échelle. Nous étayons ces affirmations par l'analyse détaillée d'un scénario de calcul haute performance et d'un scénario de grid computing