Dynamic Trace-Based Data Dependency Analysis for Parallelization of C Programs

Writing parallel code is traditionally considered a difficult task, even when it is tackled from the beginning of a project. In this paper, we demonstrate an innovative toolset that faces this challenge directly. It provides the software developers with profile data and directs them to possible top-level, pipeline-style parallelization opportunities for an arbitrary sequential C program. This approach is complementary to the methods based on static code analysis and automatic code rewriting and does not impose restrictions on the structure of the sequential code or the parallelization style, even though it is mostly aimed at coarse-grained task-level parallelization. The proposed toolset has been utilized to define parallel code organizations for a number of real-world representative applications and is based on and is provided as free source

Interactive Trace-Based Analysis Toolset for Manual Parallelization of C Programs

Massive amounts of legacy sequential code need to be parallelized to make better use of modern multiprocessor architectures. Nevertheless, writing parallel programs is still a difficult task. Automated parallelization methods can be effective both at the statement and loop levels and, recently, at the task level, but they are still restricted to specific source code constructs or application domains. We present in this article an innovative toolset that supports developers when performing manual code analysis and parallelization decisions. It automatically collects and represents the program profile and data dependencies in an interactive graphical format that facilitates the analysis and discovery of manual parallelization opportunities. The toolset can be used for arbitrary sequential C programs and parallelization patterns. Also, its program-scope data dependency tracing at runtime can complement the tools based on static code analysis and can also benefit from it at the same time. We also tested the effectiveness of the toolset in terms of time to reach parallelization decisions and of their quality. We measured a significant improvement for several real-world representative applications

Adaptive Real-Time Scheduling for Legacy Multimedia Applications

Multimedia applications are often executed on standard Personal Computers. The absence of established standards has hindered the adoption of real-time scheduling solutions in this class of applications. Developers have adopted a wide range of heuristic approaches to achieve an acceptable timing behaviour but the result is often unreliable. We propose a mechanism to extend the benefits of real-time scheduling to legacy applications based on the combination of two techniques: 1) a real-time monitor that observes and infers the activation period of the application, and 2) a feedback mechanism that adapts the scheduling parameters to improve its real-time performance

Microwave sensors for in situ monitoring of trace metals in polluted water

Thousands of pollutants are threatening our water supply, putting at risk human and environmental health. Between them, trace metals are of significant concern, due to their high toxicity at low concentrations. Abandoned mining areas are globally one of the major sources of toxic metals. Nowadays, no method can guarantee an immediate response for quantifying these pollutants. In this work, a novel technique based on microwave spectroscopy and planar sensors for in situ real-time monitoring of water quality is described. The sensors were developed to directly probe water samples, and in situ trial measurements were performed in freshwater in four polluted mining areas in the UK. Planar microwave sensors were able to detect the water pollution level with an immediate response specifically depicted at three resonant peaks in the GHz range. To the authors' best knowledge, this is the first time that planar microwave sensors were tested in situ, demonstrating the ability to use this method for classifying more and less polluted water using a multiple-peak approach

Selective Dynamic Analysis of Virtualized Whole-System Guest Environments

Dynamic binary analysis is a prevalent and indispensable technique in program analysis. While several dynamic binary analysis tools and frameworks have been proposed, all suffer from one or more of: prohibitive performance degradation, a semantic gap between the analysis code and the execution under analysis, architecture/OS specificity, being user-mode only, and lacking flexibility and extendability. This dissertation describes the design of the Dynamic Executable Code Analysis Framework (DECAF), a virtual machine-based, multi-target, whole-system dynamic binary analysis framework. In short, DECAF seeks to address the shortcomings of existing whole-system dynamic analysis tools and extend the state of the art by utilizing a combination of novel techniques to provide rich analysis functionality without crippling amounts of execution overhead. DECAF extends the mature QEMU whole-system emulator, a type-2 hypervisor capable of emulating every instruction that executes within a complete guest system environment. DECAF provides a novel, hardware event-based method of just-in-time virtual machine introspection (VMI) to address the semantic gap problem. It also implements a novel instruction-level taint tracking engine at bitwise level of granularity, ensuring that taint propagation is sound and highly precise throughout the guest environment. A formal analysis of the taint propagation rules is provided to verify that most instructions introduce neither false positives nor false negatives. DECAF’s design also provides a plugin architecture with a simple-to-use, event-driven programming interface that makes it both flexible and extendable for a variety of analysis tasks. The implementation of DECAF consists of 9550 lines of C++ code and 10270 lines of C code. Its performance is evaluated using CPU2006 SPEC benchmarks, which show an average overhead of 605% for system wide tainting and 12% for VMI. Three platformneutral DECAF plugins - Instruction Tracer, Keylogger Detector, and API Tracer - are described and evaluated in this dissertation to demonstrate the ease of use and effectiveness of DECAF in writing cross-platform and system-wide analysis tools. This dissertation also presents the Virtual Device Fuzzer (VDF), a scalable fuzz testing framework for discovering bugs within the virtual devices implemented as part of QEMU. Such bugs could be used by malicious software executing within a guest under analysis by DECAF, so the discovery, reproduction, and diagnosis of such bugs helps to protect DECAF against attack while improving QEMU and any analysis platforms built upon QEMU. VDF uses selective instrumentation to perform targeted fuzz testing, which explores only the branches of execution belonging to virtual devices under analysis. By leveraging record and replay of memory-mapped I/O activity, VDF quickly cycles virtual devices through an arbitrarily large number of states without requiring a guest OS to be booted or present. Once a test case is discovered that triggers a bug, VDF reduces the test case to the minimum number of reads/writes required to trigger the bug and generates source code suitable for reproducing the bug during debugging and analysis. VDF is evaluated by fuzz testing eighteen QEMU virtual devices, generating 1014 crash or hang test cases that reveal bugs in six of the tested devices. Over 80% of the crashes and hangs were discovered within the first day of testing. VDF covered an average of 62.32% of virtual device branches during testing, and the average test case was minimized to a reproduction test case only 18.57% of its original size