Instrumenting self-modifying code

Adding small code snippets at key points to existing code fragments is called instrumentation. It is an established technique to debug certain otherwise hard to solve faults, such as memory management issues and data races. Dynamic instrumentation can already be used to analyse code which is loaded or even generated at run time.With the advent of environments such as the Java Virtual Machine with optimizing Just-In-Time compilers, a new obstacle arises: self-modifying code. In order to instrument this kind of code correctly, one must be able to detect modifications and adapt the instrumentation code accordingly, preferably without incurring a high penalty speedwise. In this paper we propose an innovative technique that uses the hardware page protection mechanism of modern processors to detect such modifications. We also show how an instrumentor can adapt the instrumented version depending on the kind of modificiations as well as an experimental evaluation of said techniques.Comment: In M. Ronsse, K. De Bosschere (eds), proceedings of the Fifth International Workshop on Automated Debugging (AADEBUG 2003), September 2003, Ghent. cs.SE/030902

Advances in Engineering Software for Multicore Systems

The vast amounts of data to be processed by today’s applications demand higher computational power. To meet application requirements and achieve reasonable application performance, it becomes increasingly profitable, or even necessary, to exploit any available hardware parallelism. For both new and legacy applications, successful parallelization is often subject to high cost and price. This chapter proposes a set of methods that employ an optimistic semi-automatic approach, which enables programmers to exploit parallelism on modern hardware architectures. It provides a set of methods, including an LLVM-based tool, to help programmers identify the most promising parallelization targets and understand the key types of parallelism. The approach reduces the manual effort needed for parallelization. A contribution of this work is an efficient profiling method to determine the control and data dependences for performing parallelism discovery or other types of code analysis. Another contribution is a method for detecting code sections where parallel design patterns might be applicable and suggesting relevant code transformations. Our approach efficiently reports detailed runtime data dependences. It accurately identifies opportunities for parallelism and the appropriate type of parallelism to use as task-based or loop-based

A Framework for File Format Fuzzing with Genetic Algorithms

Secure software, meaning software free from vulnerabilities, is desirable in today\u27s marketplace. Consumers are beginning to value a product\u27s security posture as well as its functionality. Software development companies are recognizing this trend, and they are factoring security into their entire software development lifecycle. Secure development practices like threat modeling, static analysis, safe programming libraries, run-time protections, and software verification are being mandated during product development. Mandating these practices improves a product\u27s security posture before customer delivery, and these practices increase the difficulty of discovering and exploiting vulnerabilities. Since the 1980\u27s, security researchers have uncovered software defects by fuzz testing an application. In fuzz testing\u27s infancy, randomly generated data could discover multiple defects quickly. However, as software matures and software development companies integrate secure development practices into their development life cycles, fuzzers must apply more sophisticated techniques in order to retain their ability to uncover defects. Fuzz testing must evolve, and fuzz testing practitioners must devise new algorithms to exercise an application in unexpected ways. This dissertation\u27s objective is to create a proof-of-concept genetic algorithm fuzz testing framework to exercise an application\u27s file format parsing routines. The framework includes multiple genetic algorithm variations, provides a configuration scheme, and correlates data gathered from static and dynamic analysis to guide negative test case evolution. Experiments conducted for this dissertation illustrate the effectiveness of a genetic algorithm fuzzer in comparison to standard fuzz testing tools. The experiments showcase a genetic algorithm fuzzer\u27s ability to discover multiple unique defects within a limited number of negative test cases. These experiments also highlight an application\u27s increased execution time when fuzzing with a genetic algorithm. To combat increased execution time, a distributed architecture is implemented and additional experiments demonstrate a decrease in execution time comparable to standard fuzz testing tools. A final set of experiments provide guidance on fitness function selection with a CHC genetic algorithm fuzzer with different population size configurations

CACHE OPTIMIZATION AND PERFORMANCE EVALUATION OF A STRUCTURED CFD CODE - GHOST

This research focuses on evaluating and enhancing the performance of an in-house, structured, 2D CFD code - GHOST, on modern commodity clusters. The basic philosophy of this work is to optimize the cache performance of the code by splitting up the grid into smaller blocks and carrying out the required calculations on these smaller blocks. This in turn leads to enhanced code performance on commodity clusters. Accordingly, this work presents a discussion along with a detailed description of two techniques: external and internal blocking, for data access optimization. These techniques have been tested on steady, unsteady, laminar, and turbulent test cases and the results are presented. The critical hardware parameters which influenced the code performance were identified. A detailed study investigating the effect of these parameters on the code performance was conducted and the results are presented. The modified version of the code was also ported to the current state-of-art architectures with successful results

Dynamic slicing long running programs through execution fast forwarding

Fixing runtime bugs in long running programs using tracing based analyses such as dynamic slicing was believed to be prohibitively expensive. In this paper, we present a novel execution fast forward-ing technique that makes it feasible. While a naive solution is to divide the entire execution by checkpoints, and then apply dynamic slicing enabled by tracing on one checkpoint interval at a time, it is still too costly even with state-of-the-art tracing techniques. Our technique is derived from two key observations. The first one is that long running programs are usually driven by events, which has been taken advantage of by checkpointing/replaying techniques to deterministically replay an execution from the event log. The sec-ond observation is that all the events are not relevant to replaying a particular part of the execution, in which the programmer sus-pects an error happened. We develop a slicing-like technique on the event log such that many irrelevant events are successfully pruned. Driven by the reduced log, the replayed execution is now traced for fault location. This replayed execution has the effect of fast forwarding, i.e the amount of executed instructions is significantly reduced without losing the accuracy of reproducing the failure. We describe how execution fast forwarding is combined with check-pointing and tracing based dynamic slicing, which we believe is the first attempt to integrate these two techniques. The dynamic slices of a set of reported bugs for long running programs are studied to show the effectiveness of dynamic slicing, which is a significant step forward compared to our prior work. 1

PERFORMANCE OPTIMIZATION OF A STRUCTURED CFD CODE - GHOST ON COMMODITY CLUSTER ARCHITECTURES

This thesis focuses on optimizing the performance of an in-house, structured, 2D CFD code – GHOST, on commodity cluster architectures. The basic philosophy of the work is to optimize the cache usage of the code by implementing efficient coding techniques without changing the underlying numerical algorithm. Various optimization techniques that were implemented and the resulting changes in performance have been presented. Two techniques, external and internal blocking that were implemented earlier to tune the performance of this code have been reviewed. What follows is further tuning effort in order to circumvent the problems associated with using the blocking techniques. Later, to establish the universality of the optimization techniques, testing has been done on more complicated test case. All the techniques presented in this thesis have been tested on steady, laminar test cases. It has been proved that optimized versions of the code achieve better performances on variety of commodity cluster architectures chosen in this study