Information visualizations must allow users to browse information spaces and focus quickly on items of interest. Being able to see some representation of the entire information space provides an initial gestalt overview and gives context to support browsing and search tasks. However, the limited number of pixels on the screen constrain the information bandwidth and make it difficult to completely display large information spaces. The Information Mural is a two-dimensional, reduced representation of an entire information space that fits entirely within a display window or screen. The mural creates a miniature version of the information space using visual attributes such as grayscale shading, intensity, color, and pixel size, along with anti-aliased compression techniques. Information Murals can be used as stand-alone visualizations or in global navigational views. We have built several prototypes to demonstrate the use of Information Murals in visualization applications; subject matter ...

Current trends in high performance computing suggest that users will soon have widespread access to clusters of multiprocessors with hundreds, if not thousands, of processors. This unprecedented degree of parallelism will undoubtedly expose scalability limitations in existing applications, where scalability is the ability of a parallel algorithm on a parallel architecture to effectively utilize an increasing number of processors. Users will need precise and automated techniques for detecting the cause of limited scalability. This paper addresses this dilemma. First, we argue that users face numerous challenges in understanding application scalability: managing substantial amounts of experiment data, extracting useful trends from this data, and reconciling performance information with their application's design. Second, we propose a solution to automate this data analysis problem by applying fundamental statistical techniques to scalability experiment data. Finally, we evaluate our operational prototype on several applications, and show that statistical techniques offer an effective strategy for assessing application scalability. In particular, we find that non-parametric correlation of the number of tasks to the ratio of the time for communication operations to overall communication time provides a reliable measure for identifying communication operations that scale poorly. 1

We present a technique for performance analysis that helps users understand the communication behavior of their message passing applications. Our method automatically classifies individual communication operations and it reveals the cause of communication inefficiencies in the application. This classification allows the developer to focus quickly on the culprits of truly inefficient behavior, rather than manually foraging through massive amounts of performance data. Specifically, we trace the message operations of MPI applications and then classify each individual communication event using decision tree classification, a supervised learning technique. We train our decision tree using microbenchmarks that demonstrate both efficient and inefficient communication. Since our technique adapts to the target system's configuration through these microbenchmarks, we can simultaneously automate the performance analysis process and improve classification accuracy. Our experiments on four applications demonstrate that our technique can improve the accuracy of performance analysis, and dramatically reduce the amount of data that users must encounter.

Performance analysis of communication activity for a terascale application with traditional message tracing can be overwhelming in terms of overhead, perturbation, and storage. We propose a novel alternative that enables dynamic statistical profiling of an application's communication activity using message sampling. We have implemented an operational prototype, named PHOTON, and our evidence shows that this new approach can provide an accurate, low-overhead, tractable alternative for performance analysis of communication activity. PHOTON consists of two components: a Message Passing Interface (MPI) profiling layer that implements sampling and analysis, and a modified MPI runtime that appends a small but necessary amount of information to individual messages. More importantly, this alternative enables an assortment of runtime analysis techniques so that, in contrast to post-mortem, trace-based techniques, the raw performance data can be jettisoned immediately after analysis. Our investigation shows that message sampling can reduce overhead to imperceptible levels for many applications. Experiments on several applications demonstrate the viability of this approach. For example, with one application, our technique reduced the analysis overhead from 154% for traditional tracing to 6% for statistical profiling. We also evaluate different sampling techniques in this framework. The coverage of the sample space provided by purely random sampling is superior to counter- and timer-based sampling. Also, PHOTON'S design reveals that frugal modifications to the MPI rtmtime system could facilitate such techniques on production computing systems, and it suggests that this sampling technique could execute continuously for longrunning applications.

. We report on our experiences in building a computational environment for tomographic image analysis for marine seismologists studying the structure and evolution of mid-ocean ridge volcanism. The computational environment is determined by an evolving set of requirements for this problem domain and includes needs for high-performance parallel computing, large data analysis, model visualization, and computation interaction and control. Although these needs are not unique in scientific computing, the integration of techniques for seismic tomography with tools for parallel computing and data analysis into a computational environment was (and continues to be) an interesting, important learning experience for researchers in both disciplines. For the geologists, the use of the environment led to fundamental geologic discoveries on the East Pacific Rise, the improvement of parallel ray tracing algorithms, and a better regard for the use of computational steering in aiding model convergence. ...

This paper investigates the visualization of distributed algorithms. We present a conceptual model and a system, VADE, that realizes this model. Since in asynchronous distributed systems there is no way of knowing (let alone, visualizing) the "real" execution, we show how to generate a visualization which is consistent with the execution of the distributed algorithm. We also present the design and implementation of our system. VADE is designed so that the algorithm runs on the server's machines while the visualization is executed on a web page on the client's machine. Programmers can write animations quickly and easily with the assistant of the VADE's libraries. Keywords: visualization of algorithms, program visualization, algorithm animation, visualization in education, distributed algorithms. 1 INTRODUCTION Algorithm visualization can assist in the design of algorithms, in the debug process, and while teaching algorithms to students and colleagues. When distribution is added to the ...

this article consolidates all of the meta-studys principal data for easy access. Readers interested in further scrutinizing the numerical counts that are graphed and discussed in Sections 3 and 4 can consult the appendix, which indicates the precise manner in which each of the SV effectiveness studies is classified

This paper describes the design and implementation of a high-level visualization programming system called Viz. Viz was created out of a need to support rapid visualization prototyping in an environment that could be extended by abstractions in the application problem domain. Viz provides this in a programming environment built on a high-level, interactive language (Scheme) that embeds a 3D graphics library (Open Inventor), and that utilizes a data reactive model of visualization operation to capture mechanisms that have been found to be important in visualization design (e.g., constraints, controlled data flow, dynamic analysis, animation). The strength of Viz is in its ability to create non-trivial visualizations rapidly and to construct libraries of 3D graphics functionality easily. Although our original focus was on parallel program and performance data visualization, Viz applies beyond these areas. We show several examples that highlight Viz functionality and the visualization des...

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The performance attained by parallel programs executed on multiprocessor systems is largely in uenced both by the characteristics of the code and by those of the system architecture. Indeed, parallel machines are typically message{passing systems consisting of hundreds of processing elements. Implementing high performance parallel applications is a di cult task and understanding sources of poor e ciency represents a key factor in every evaluation process. Performance analysis studies can be carried out by means of an integrated use of statistical and numerical techniques together with visualization methods. In this paper, the potentialities of this approach are presented by means of a case study dealing with a real kernel code used in weather forecasts. 1