Towards Visualization Scalability through Time Intervals and Hierarchical Organization of Monitoring Data

Highly distributed systems such as Grids are used today to the execution of large-scale parallel applications. The behavior analysis of these applications is not trivial. The complexity appears because of the event correlation among processes, external influences like time-sharing mechanisms and saturation of network links, and also the amount of data that registers the application behavior. Almost all visualization tools to analysis of parallel applications offer a space-time representation of the application behavior. This paper presents a novel technique that combines traces from grid applications with a treemap visualization of the data. With this combination, we dynamically create an annotated hierarchical structure that represents the application behavior for the selected time interval. The experiments in the grid show that we can readily use our technique to the analysis of large-scale parallel applications with thousands of processes.

[1]  Wolfgang E. Nagel,et al.  Performance Optimization for Large Scale Computing: The Scalable VAMPIR Approach , 2001, International Conference on Computational Science.

[2]  Toni Cortes,et al.  PARAVER: A Tool to Visualize and Analyze Parallel Code , 2007 .

[3]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[4]  Lucas Mello Schnorr,et al.  JRastro: a trace agent for debugging multithreaded and distributed Java programs , 2003, Proceedings. 15th Symposium on Computer Architecture and High Performance Computing.

[5]  Jarke J. van Wijk,et al.  Squarified Treemaps , 2000, VisSym.

[6]  Lucas Mello Schnorr,et al.  DIMVisual: Data Integration Model for Visualization of Parallel Programs Behavior , 2006, Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06).

[7]  Bernd Mohr,et al.  A Scalable Approach to MPI Application Performance Analysis , 2005, PVM/MPI.

[8]  William Gropp,et al.  Toward Scalable Performance Visualization with Jumpshot , 1999, Int. J. High Perform. Comput. Appl..

[9]  Jacques Chassin de Kergommeaux,et al.  Flexible performance visualization of parallel and distributed applications , 2003, Future Gener. Comput. Syst..

[10]  Interner Bericht.  VAMPIR: Visualization and Analysis of MPI Resources , 1996 .

[11]  George Nagy,et al.  HIERARCHICAL REPRESENTATION OF OPTICALLY SCANNED DOCUMENTS , 1984 .

[12]  Laxmikant V. Kalé,et al.  Towards scalable performance analysis and visualization through data reduction , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[13]  D.A. Reed,et al.  Scalable performance analysis: the Pablo performance analysis environment , 1993, Proceedings of Scalable Parallel Libraries Conference.

[14]  Jacques Chassin de Kergommeaux,et al.  Pajé, an interactive visualization tool for tuning multi-threaded parallel applications , 2000, Parallel Comput..

[15]  A. Karr,et al.  Visual Scalability , 2002 .

[16]  Ben Shneiderman,et al.  Tree-maps: a space-filling approach to the visualization of hierarchical information structures , 1991, Proceeding Visualization '91.

[17]  Ben Shneiderman,et al.  Tree visualization with tree-maps: 2-d space-filling approach , 1992, TOGS.

[18]  Thierry Gautier,et al.  KAAPI: A thread scheduling runtime system for data flow computations on cluster of multi-processors , 2007, PASCO '07.