A hierarchical aggregation model to achieve visualization scalability in the analysis of parallel applications

Schnorr, Lucas Mello; Huard, Guillaume; Navaux, Philippe O. A.

doi:10.1016/j.parco.2011.12.001

Cited by 12 publications

(16 citation statements)

References 15 publications

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“…Therefore, the abstraction of the resources, and their representation, can also be important to reduce the complexity of the trace display. Grouping resources, filtering of uninteresting resources, or hierarchical organization of resources are the related techniques used in the reviewed literature.…”

Section: Multilevel Trace Abstraction Techniquesmentioning

confidence: 99%

“…Schnorr et al group the threads on the basis of their associated processes and then by machine name, cluster, and so on. Automatic clustering of system resources is performed in the work of Black et al Two approaches were used: automatic clustering of processes based on their runtime inter‐process communication intensity, and combining the inter‐process communication information and the information of processes extracted from static source code…”

Section: Multilevel Trace Abstraction Techniquesmentioning

confidence: 99%

“…Triva is another tool that aims to visualize the trace events of large parallel applications in a hierarchical manner, supporting visualization scalability. It supports the dynamic hierarchical organization of trace data and uses the Treemap space filling technique to visualize trace data.…”

Section: Multilevel Trace Visualizationmentioning

confidence: 99%

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Multi‐scale navigation of large trace data: A survey

Ezzati‐Jivan

Dagenais

2017

Concurrency and Computation

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Summary Dynamic analysis through execution traces is frequently used to analyze the runtime behavior of software systems. However, tracing long running executions generates voluminous data, which are complicated to analyze and manage. Extracting interesting performance or correctness characteristics out of large traces of data from several processes and threads is a challenging task. Trace abstraction and visualization are potential solutions to alleviate this challenge. Several efforts have been made over the years in many subfields of computer science for trace data collection, maintenance, analysis, and visualization. Many analyses start with an inspection of an overview of the trace, before digging deeper and studying more focused and detailed data. These techniques are common and well supported in geographical information systems, automatically adjusting the level of details depending on the scale. However, most trace visualization tools operate at a single level of representation, which are not adequate to support multilevel analysis. Sophisticated techniques and heuristics are needed to address this problem. Multi‐scale (multilevel) visualization with support for zoom and focus operations is an effective way to enable this kind of analysis. Considerable research and several surveys are proposed in the literature in the field of trace visualization. However, multi‐scale visualization has yet received little attention. In this paper, we provide a survey and methodological structure for categorizing tools and techniques aiming at multi‐scale abstraction and visualization of execution trace data and discuss the requirements and challenges faced to be able to meet evolving user demands.

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Section: Multilevel Trace Abstraction Techniquesmentioning

confidence: 99%

Section: Multilevel Trace Abstraction Techniquesmentioning

confidence: 99%

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Multi‐scale navigation of large trace data: A survey

Ezzati‐Jivan

Dagenais

2017

Concurrency and Computation

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“…It can be seen that: Most of the aforementioned research cannot observe the mobile group with dynamic variation, especially in the environment with the application of stream data. In literature (Schnorr, Huard and Navaux, 2012;Linhart and Skogestad, 2012;Ben Makhlouf and Hamdi, 2014) the concept of athlete trajectory aggregation model and a kind of mobile group detection algorithm is applied for the mining of the aggregation model of the moving object. The aggregation model can be used for the statistics or prediction of the possible accidents and congestion phenomenon that may occur in the traffic system.…”

Section: Introductionmentioning

confidence: 99%

Motion Trajectory Aggregation Model of Playing Field Based on Spatial Temporal Features

2016

Rev. Téc. Ing. Univ. Zulia

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Volleyball athletes playing filed motion trajectory aggregation model refers to a group of movement patterns of the volleyball athletes on the playing field, which is usually used to predict the abnormal phenomenon that is occurred in the model. It is difficult for the existing research method to conduct mining on the specific aggregation model accurately and highly efficiently. For this purpose, a kind of athlete trajectory aggregation model mining method based on the spatial temporal features is put forward, which first makes the analysis on the trajectory data by the improved method of Spatial clustering algorithm (SPC), so as to obtain the playing field motion trajectory clustering; Then, by the application of the spatial temporal feature model to replace the method of separate storage for the trajectory data, to carry out real-time observation on the spatial and temporal variation features of the playing field motion trajectory clustering. Experimental results show that, the proposed method is significantly superior to other methods in view of the accuracy and efficiency for the mining of volleyball athlete trajectory aggregation model.

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“…In case of large-scale applications, since the amount of recorded information exceeds the display capacity [1], visualization tools implement reduction techniques to offer a proper rendering [2]. To that extent, data aggregation provides macroscopic representations by averaging the system states and dynamics while preserving some of their essential spatial and temporal features.…”

Section: Introductionmentioning

confidence: 99%

Evaluating trace aggregation for performance visualization of large distributed systems

Lamarche-Perrin

Schnorr

Vincent

et al. 2014

2014 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)

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Performance analysis through visualization techniques usually suffers semantic limitations due to the size of parallel applications. Most performance visualization tools rely on data aggregation to work at scale, without any attempt to evaluate the loss of information caused by such aggregations. This paper proposes a technique to evaluate the quality of aggregated representations -using measures from information theoryand to optimize such measures in order to build consistent multiresolution representations of large execution traces.

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A hierarchical aggregation model to achieve visualization scalability in the analysis of parallel applications

Cited by 12 publications

References 15 publications

Multi‐scale navigation of large trace data: A survey

Multi‐scale navigation of large trace data: A survey

Motion Trajectory Aggregation Model of Playing Field Based on Spatial Temporal Features

Evaluating trace aggregation for performance visualization of large distributed systems

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