As computer systems continue to grow rapidly in both complexity and scale, developers need tools to help them understand the behavior and performance of these systems. While information visualization is a promising technique, most existing computer systems visualizations have focused on very specific problems and data sources, limiting their applicability.
This dissertation introduces Rivet, a general-purpose environment for the development of computer systems visualizations. Rivet can be used for both real-time and post-mortem analyses of data from a wide variety of sources. The modular architecture of Rivet enables sophisticated visualizations to be assembled using simple building blocks representing the data, the visual representations, and the mappings between them. The implementation of Rivet enables the rapid prototyping of visualizations through a scripting language interface while still providing high-performance graphics and data management.
The effectiveness of Rivet as a tool for computer systems analysis is demonstrated through a collection of case studies. Visualizations created using Rivet have been used to display: (a) line-by-line execution data from the SUIF Explorer interactive parallelizing compiler, enabling programmers to maximize the parallel speedups of their applications; (b) detailed memory system utilization data from the FlashPoint memory profiler, providing insights on both sequential and parallel program bottlenecks; (c) the behavior of applications running on superscalar processors, allowing developers to take full advantage of these complex CPUs; and (d) the real-time performance of computer systems and clusters, drawing attention to interesting or anomalous behavior. In addition to these focused examples, Rivet has been also used in conjunction with more comprehensive data sources such as the SimOS complete machine simulator. A detailed performance analysis of the Argus parallel graphics library demonstrates how these tools combine to provide a powerful iterative analysis framework for understanding computer systems as a whole.