Doctor of Philosophy

dissertationComputer programs have complex interactions with their underlying hardware, exhibiting complex behaviors as a result. It is critical to understand these programs, as they serve an importantrole: researchers use them to express new ideas in computer science, while many others derive production value from them. In both cases, program understanding leads to mastery over these functions, adding value to human endeavors. Memory behavior is one of the hallmarks of general program behavior: it represents the critical function of retrieving data for the program to work on; it often reflects the overall actions taken by the program, providing a signature of program behavior; and it is often an important performance bottleneck, as the the memory subsystem is typically much slower than the processor. These reasons justify an investigation into the memory behavior of programs. A memory reference trace is a list of memory transactions performed by a program at runtime, a rich data source capturing the whole of a program's interaction with the memory subsystem, and a clear starting point for investigating program memory behavior. However, such a trace is extremely difficult to interpret by mere inspection, as it consists solely of many, many addresses and operation codes, without any more structure or context. This dissertation proposes to use visualization to construct images and animations of the data within a reference trace, thereby visually transmitting structures and events as encoded in the trace. These visualization approaches are designed with different focuses, meant to expose various aspects of the trace. For instance, the time dimension of the reference traces can be handled either with animation, showing events as they occur, or by laying time out in a spatial dimension, giving a view of the entire history of the trace at once. The approaches also vary in their level of abstraction from the hardware: some are concretely connected to representations of the memory itself, while others are more free-form, using more abstract metaphors to highlight general behaviors and patterns, which in turn characterize the program behavior. Each approach delivers its own set of insights, as demonstrated in this dissertation

Autonomic visualisation.

This thesis introduces the concept of autonomic visualisation, where principles of autonomic systems are brought to the field of visualisation infrastructure. Problems in visualisation have a specific set of requirements which are not always met by existing systems. The first half of this thesis explores a specific problem for large scale visualisation; that of data management. Visualisation algorithms have somewhat different requirements to other external memory problems, due to the fact that they often require access to all, or a large subset, of the data in a way that is highly dependent on the view. This thesis proposes a knowledge-based approach to pre-fetching in this context, and presents evidence that such an approach yields good performance. The knowledge based approach is incorporated into a five-layer model, which provides a systematic way of categorising and designing out-of-core, or external memory, systems. This model is demonstrated with two example implementations, on in the local and one in the remote context. The second half explores autonomic visualisation in the more general case. A simulation tool, created for the purpose of designing autonomic visualisation infrastructure is presented. This tool, SimEAC, provides a way of facilitating the development of techniques for managing large-scale visualisation systems. The abstract design of the simulation system, as well as details of the implementation are presented. The architecture of the simulator is explored, and then the system is evaluated in a number of case studies indicating some of the ways in which it can be used. The simulator provides a framework for experimentation and rapid prototyping of large scale autonomic systems