Algorithmic Debugging of Real-World Haskell Programs: Deriving Dependencies from the Cost Centre Stack

Existing algorithmic debuggers for Haskell require a transformation of all modules in a program, even libraries that the user does not want to debug and which may use language features not supported by the debugger. This is a pity, because a promising ap- proach to debugging is therefore not applicable to many real-world programs. We use the cost centre stack from the Glasgow Haskell Compiler profiling environment together with runtime value observations as provided by the Haskell Object Observation Debugger (HOOD) to collect enough information for algorithmic debugging. Program annotations are in suspected modules only. With this technique algorithmic debugging is applicable to a much larger set of Haskell programs. This demonstrates that for functional languages in general a simple stack trace extension is useful to support tasks such as profiling and debugging

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

MadT: A Memory Access Detection Tool for Symbolic Memory Profiling

Tools for memory access detection are widely used, playing an important role especially in real-time systems. For example, on multi-core platforms, the problem of co-scheduling CPU and memory resources with hard real-time constraints requires a deep understanding of the memory access patterns of the deployed taskset. While code execution flow can be analyzed by considering the control-flow graph and reasoning in terms of basic blocks, a similar approach cannot apply to data accesses. In this paper, we propose MadT, a tool that uses a novel mechanism to perform memory access detection of general purpose applications. MadT does not perform binary instrumentation and always executes application code natively on the platform. Hence it can operate entirely in user-space without sand-boxing the task under analysis. Furthermore, MadT provides detailed symbolic information about the accessed memory structures, so it is able to translate the virtual addresses to their original symbolic variable names. Finally, it requires no modifications to application source code. The proposed methodology relies on existing OS-level capabilities. In this paper, we describe how MadT can be implemented on commercial hardware and we compare its performance with state-of-the-art software techniques for memory access detection.CNS-1302563CNS-1219064Ope

System Level Performance Evaluation of Distributed Embedded Systems

In order to evaluate the feasibility of the distributed embedded systems in different application domains at an early phase, the System Level Performance Evaluation (SLPE) must provide reliable estimates of the nonfunctional properties of the system such as end-to-end delays and packet losses rate. The values of these non-functional properties depend not only on the application layer of the OSI model but also on the technologies residing at the MAC, transport and Physical layers. Therefore, the system level performance evaluation methodology must provide functionally accurate models of the protocols and technologies operating at these layers. After conducting a state of the art survey, it was found that the existing approaches for SLPE are either specialized for a particular domain of systems or apply a particular model of computation (MOC) for modeling the communication and synchronization between the different components of a distributed application. Therefore, these approaches abstract the functionalities of the data-link, Transport and MAC layers by the highly abstract message passing methods employed by the different models of computation. On the other hand, network simulators such as OMNeT++, ns-2 and Opnet do not provide the models for platform components of devices such as processors and memories and totally abstract the application processing by delays obtained via traffic generators. Therefore the system designer is not able to determine the potential impact of an application in terms of utilization of the platform used by the device. Hence, for a system level performance evaluation approach to estimate both the platform utilization and the non-functional properties which are a consequence of the lower layers of OSI models (such as end-to-end delays), it must provide the tools for automatic workload extraction of application workload models at various levels of refinement and functionally correct models of lower layers of OSI model (Transport MAC and Physical layers). Since ABSOLUT is not restricted to a particular domain and also does not depend on any MOC, therefore it was selected for the extension to a system level performance evaluation approach for distributed embedded systems. The models of data-link and Transport layer protocols and automatic workload generation of system calls was not available in ABSOLUT performance evaluation methodology. The, thesis describes the design and modelling of these OSI model layers and automatic workload generation tool for system calls. The tools and models integrated to ABSOLUT methodology were used in a number of case studies. The accuracy of the protocols was compared to network simulators and real systems. The results were 88% accurate for user space code of the application layer and provide an improvement of over 50% as compared to manual models for external libraries and system calls. The ABSOLUT physical layer models were found to be 99.8% accurate when compared to analytical models. The MAC and transport layer models were found to be 70-80% accurate when compared with the same scenarios simulated by ns-2 and OMNeT++ simulators. The bit error rates, frame error probability and packet loss rates show close correlation with the analytical methods .i.e., over 99%, 92% and 80% respectively. Therefore the results of ABSOLUT framework for application layer outperform the results of performance evaluation approaches which employ virtual systems and at the same time provide as accurate estimates of the end-to-end delays and packet loss rate as network simulators. The results of the network simulators also vary in absolute values but they follow the same trend. Therefore, the extensions made to ABSOLUT allow the system designer to identify the potential bottlenecks in the system at different OSI model layers and evaluate the non-functional properties with a high level of accuracy. Also, if the system designer wants to focus entirely on the application layer, different models of computations can be easily instantiated on top of extended ABSOLUT framework to achieve higher simulation speeds as described in the thesis

Doctor of Philosophy

dissertationCurrent scaling trends in transistor technology, in pursuit of larger component counts and improving power efficiency, are making the hardware increasingly less reliable. Due to extreme transistor miniaturization, it is becoming easier to flip a bit stored in memory elements built using these transistors. Given that soft errors can cause transient bit-flips in memory elements, caused due to alpha particles and cosmic rays striking those elements, soft errors have become one of the major impediments in system resilience as we move towards exascale computing. Soft errors escaping the hardware-layer may silently corrupt the runtime application data of a program, causing silent data corruption in the output. Also, given that soft errors are transient in nature, it is notoriously hard to trace back their origins. Therefore, techniques to enhance system resilience hinge on the availability of efficient error detectors that have high detection rates, low false positive rates, and lower computational overhead. It is equally important to have a flexible infrastructure capable of simulating realistic soft error models to promote an effective evaluation of newly developed error detectors. In this work, we present a set of techniques for efficiently detecting soft errors affecting control-flow, data, and structured address computations in an application. We evaluate the efficacy of the proposed techniques by evaluating them on a collection of benchmarks through fault-injection driven studies. As an important requirement, we also introduce two new LLVM-based fault injectors, KULFI and VULFI, which are geared towards scalar and vector architectures, respectively. Through this work, we aim to make contributions to the system resilience community by making our research tools (in the form of error detectors and fault injectors) publicly available

New techniques for adaptive program optimization

Adaptive optimization technology is a key ingredient in modern runtime systems. This technology aims at improving performance by making optimization decisions on the basis of a program’s observed behavior. Application virtual machines indeed face different and perhaps more compelling issues compared to traditional static optimizers, as dynamic language features can force the deferral of most effective optimizations until run time. In this thesis, we present novel ideas to improve adaptive optimization, focusing on two main problems: collecting fine-grained program profiles with low overhead to guide feedback-directed optimization, and supporting continuous optimization and deoptimization by diverting execution across dynamically generated code versions. We present two profiling techniques: the first works at inter-procedural level to collect calling context information for hot code portions, while the second captures cyclic-path profiles within a function’s boundaries. Both techniques rely on efficient and elegant data structures, advancing the state of the art of the theory and practice of the performance profiling literature. We then focus our attention on supporting continuous optimization through on-stack replacement (OSR) mechanisms. We devise a new OSR framework encoded entirely at intermediate-representation level, which extends the best OSR practices with the ability to perform OSR at nearly any program location. Our techniques pave the road to aggressive optimizations and debugging techniques that were not supported by previous approaches. The main technical challenge is how to automatically generate compensation code to fix the program’s state across an OSR transition between different code versions. We present a conceptual framework for OSR, distilling its essence to a core calculus with an operational semantics. Using bisimulation techniques, we describe how OSR can be correctly supported in the presence of common compiler optimizations, providing the first soundness results in this context. We implement our ideas in production systems such as Jikes RVM and the LLVM compiler toolchain, and evaluate their performance against a variety of prominent benchmarks. We investigate the end-to-end utility of our techniques in a series of case studies: we illustrate two possible applications of multi-iteration path profiling, and show how our OSR techniques advance the state of the art for MATLAB code optimization and for source-level debugging of optimized code. Part of the results of this thesis have been published in PLDI, OOPSLA, CGO, and Software Practice and Experience

An input centric paradigm for program dynamic optimizations and lifetime evolvement

Accurately predicting program behaviors (e.g., memory locality, method calling frequency) is fundamental for program optimizations and runtime adaptations. Despite decades of remarkable progress, prior studies have not systematically exploited the use of program inputs, a deciding factor of program behaviors, to help in program dynamic optimizations. Triggered by the strong and predictive correlations between program inputs and program behaviors that recent studies have uncovered, the dissertation work aims to bring program inputs into the focus of program behavior analysis and program dynamic optimization, cultivating a new paradigm named input-centric program behavior analysis and dynamic optimization.;The new optimization paradigm consists of three components, forming a three-layer pyramid. at the base is program input characterization, a component for resolving the complexity in program raw inputs and extracting important features. In the middle is input-behavior modeling, a component for recognizing and modeling the correlations between characterized input features and program behaviors. These two components constitute input-centric program behavior analysis, which (ideally) is able to predict the large-scope behaviors of a program\u27s execution as soon as the execution starts. The top layer is input-centric adaptation, which capitalizes on the novel opportunities created by the first two components to facilitate proactive adaptation for program optimizations.;This dissertation aims to develop this paradigm in two stages. In the first stage, we concentrate on exploring the implications of program inputs for program behaviors and dynamic optimization. We construct the basic input-centric optimization framework based on of line training to realize the basic functionalities of the three major components of the paradigm. For the second stage, we focus on making the paradigm practical by addressing multi-facet issues in handling input complexities, transparent training data collection, predictive model evolvement across production runs. The techniques proposed in this stage together cultivate a lifelong continuous optimization scheme with cross-input adaptivity.;Fundamentally the new optimization paradigm provides a brand new solution for program dynamic optimization. The techniques proposed in the dissertation together resolve the adaptivity-proactivity dilemma that has been limiting the effectiveness of existing optimization techniques. its benefits are demonstrated through proactive dynamic optimizations in Jikes RVM and version selection using IBM XL C Compiler, yielding significant performance improvement on a set of Java and C/C++ programs. It may open new opportunities for a broad range of runtime optimizations and adaptations. The evaluation results on both Java and C/C++ applications demonstrate the new paradigm is promising in advancing the current state of program optimizations

The hArtes Tool Chain

This chapter describes the different design steps needed to go from legacy code to a transformed application that can be efficiently mapped on the hArtes platform

Object-Centric Reflection: Unifying Reflection and Bringing It Back to Objects

Reflective applications are able to query and manipulate the structure and behavior of a running system. This is essential for highly dynamic software that needs to interact with objects whose structure and behavior are not known when the application is written. Software analysis tools, like debuggers, are a typical example. Oddly, although reflection essentially concerns run-time entities, reflective applications tend to focus on static abstractions, like classes and methods, rather than objects. This is phenomenon we call the object paradox, which makes developers less effective by drawing their attention away from run-time objects. To counteract this phenomenon, we propose a purely object-centric approach to reflection. Reflective mechanisms provide object-specific capabilities as another feature. Object-centric reflection proposes to turn this around and put object-specific capabilities as the central reflection mechanism. This change in the reflection architecture allows a unification of various reflection mechanisms and a solution to the object paradox. We introduce Bifr\"ost, an object-centric reflective system based on first-class meta-objects. Through a series of practical examples we demonstrate how object-centric reflection mitigates the object paradox by avoiding the need to reflect on static abstractions. We survey existing approaches to reflection to establish key requirements in the domain, and we show that an object-centric approach simplifies the meta-level and allows a unification of the reflection field. We demonstrate how development itself is enhanced with this new approach: talents are dynamically composable units of reuse, and object-centric debugging prevents the object paradox when debugging. We also demonstrate how software analysis is benefited by object-centric reflection with Chameleon, a framework for building object-centric analysis tools and MetaSpy, a domain-specific profile