Understanding program structure and behavior

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 117-122).A large software system usually has structure in it. Several functions work together to accomplish a certain task, and several tasks are grouped together to perform a bigger task. In order to understand this division, one has to consult the documentations or read through the source code. However, the documentations are usually incomplete and outdated, while code inspection is tedious and impractical. Algorithms have been proposed that automatically group functions with similar functionality. In this thesis I will present LogiView, an algorithm that presents an organizational view of the functions. This view will ease the process of understanding the structures in the program, identifying functions with related tasks, and separating the functions into logical groups. I will also present a methodology of analyzing the function names in the program. This method leverages the result of the LogiView algorithm and identifies the names that are most relevant to the functionality of the program. Given a set of programs that are known to have same functionality, this method extracts the similarity in the function names and builds a dictionary of the names that are semantically related to the functionality.(cont.) The methodology also detects when two programs have similar functionality and how to measure the similarity between multiple programs. Lastly, I will show that the result of the LogiView algorithm can be used to explain program performance. The abrupt change in the program behavior and repetitions of past behaviors can be explained in terms of function calls, and these functions are identified by the LogiView algorithm.by Sie Hendrata Dharmawan.M.Eng

Profiling over Adaptive Ranges Shashidhar Mysore

Modern computer systems are called on to deal with billions of events every second, whether they are instructions executed, memory locations accessed, or packets forwarded. This presents a serious challenge to those who seek to quantify, analyze, or optimize such systems, because important trends and behaviors may easily be lost in a sea of data. We present Range Adaptive Profiling (RAP) as a new and general purpose profiling method capable of hierarchically classifying streams of data efficiently in hardware. Through the use of RAP, events in an input stream are dynamically classified into increasingly precise categories based on the frequency with which they occur. The more important a class, or range of events, the more precisely it is quantified. Despite the dynamic nature of our technique, we build upon tight theoretic bounds covering both worst-case error as well as the required memory. In the limit, it is known that error and the memory bounds can be independent of the stream size, and grow only linearly with the level of precision desired. Significantly, we expose the critical constants in these algorithms and through careful engineering, algorithm re-design, and use of heuristics, we show how a high performance profile system can be implemented for Range Adaptive Profiling. RAP can be used on various profiles such as PCs, load values, and memory addresses, and has a broad range of uses, from hot-region profiling to quantifying cache miss value locality. We propose two methods of implementation, one in software and the other with specialized hardware, and we show that with just 8k bytes of memory range profiles can be gathered with an average accuracy of 98%