In this paper, we discuss ø (TAU, Tuning and Analysis Utilities), the first prototype of an integrated and portable program analysis environment for pC++ , a parallel object-oriented language system. ø is unique in that it was developed specifically for pC++ and relies heavily on pC++ 's compiler and transformation tools (specifically, the Sage ++ toolkit) for its implementation. This tight integration allows ø to achieve a combination of portability, functionality, and usability not commonly found in high-level language environments. The paper describes the design and functionality of ø , using a new tool for breakpoint-based program analysis as an example of ø 's capabilities. 1 Introduction The trend towards using high-level parallel language systems to program scalable parallel computers must be accompanied by advances in the tools and environments for program analysis and tuning. The language system concerns are achieving programmability through parallel programming abstractions...

Understanding and interpreting a large data source is an important but challenging operation in many technical disciplines. Computer visualization has become a valuable tool to help capture and portray characteristics of large data sets. In software visualization, illustrating the operation of very large programs or programs working on very large data sets has remained one of the key open problems. Here, we introduce an approach that uses semantic zooming to depict large program executions. Our method utilizes abstract, clustered graphics to portray program operations on the entire data set. Then, by interacting with the presentation, a viewer can zoom in to examine details and individual values. At this "magnified" level, the presentation adjusts to reflect displays common in existing algorithm animation and program visualization systems. 1 Introduction Software Visualization is the use of visualization and animation techniques to help people understand the characteristics and exec...

. 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. ...

. Performance extrapolation is the process of evaluating the performance of a parallel program in a target execution environment using performance information obtained for the same program in a different environment. Performance extrapolation techniques are suited for rapid performance tuning of parallel programs, particularly when the target environment is unavailable. This paper describes one such technique that was developed for data-parallel C++ programs written in the pC++ language. In pC++, the programmer can distribute a collection of objects to various processors and can have methods invoked on those objects execute in parallel. Using performance extrapolation in the development of pC++ applications allows tuning decisions to be made in advance of detailed execution measurements. The pC++ language system includes t, an integrated environment for analyzing and tuning the performance of pC++ programs. This paper presents speedy, a new addition to t, that predicts the performa...

Performance prediction is necessary in order to deal with multi-dimensional performance effects on parallel systems. The compiler-generated analytical model developed in this paper accounts for the effects of cache behavior, CPU execution time and message passing overhead for real programs written in high level data-parallel languages. The performance prediction technique is shown to be effective in analyzing several nontrivial data-parallel applications as the problem size and number of processors vary. We leverage technology from the Maple symbolic manipulation system and the S-PLUS statistical package in order to present users with critical performance information necessary for performance debugging, architectural enhancement and procurement of parallel systems. The usability of these results is improved through specifying confidence intervals as well as predicted execution times for data-parallel applications.

this paper we examine and evaluate an experimental programming environment designed at the University of Oregon that address some of these issues. This system, called

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...