Efficient Race Detection with Futures

This paper addresses the problem of provably efficient and practically good on-the-fly determinacy race detection in task parallel programs that use futures. Prior works determinacy race detection have mostly focused on either task parallel programs that follow a series-parallel dependence structure or ones with unrestricted use of futures that generate arbitrary dependences. In this work, we consider a restricted use of futures and show that it can be race detected more efficiently than general use of futures. Specifically, we present two algorithms: MultiBags and MultiBags+. MultiBags targets programs that use futures in a restricted fashion and runs in time $O(T_1 \alpha(m,n))$, where $T_1$ is the sequential running time of the program, $\alpha$ is the inverse Ackermann's function, $m$ is the total number of memory accesses, $n$ is the dynamic count of places at which parallelism is created. Since $\alpha$ is a very slowly growing function (upper bounded by $4$ for all practical purposes), it can be treated as a close-to-constant overhead. MultiBags+ an extension of MultiBags that target programs with general use of futures. It runs in time $O((T_1+k^2)\alpha(m,n))$ where $T_1$, $\alpha$, $m$ and $n$ are defined as before, and $k$ is the number of future operations in the computation. We implemented both algorithms and empirically demonstrate their efficiency

Easier Parallel Programming with Provably-Efficient Runtime Schedulers

Over the past decade processor manufacturers have pivoted from increasing uniprocessor performance to multicore architectures. However, utilizing this computational power has proved challenging for software developers. Many concurrency platforms and languages have emerged to address parallel programming challenges, yet writing correct and performant parallel code retains a reputation of being one of the hardest tasks a programmer can undertake. This dissertation will study how runtime scheduling systems can be used to make parallel programming easier. We address the difficulty in writing parallel data structures, automatically finding shared memory bugs, and reproducing non-deterministic synchronization bugs. Each of the systems presented depends on a novel runtime system which provides strong theoretical performance guarantees and performs well in practice

Provably and Practically Efficient Race Detection for Task-Parallel Code

Parallel systems are pervasive nowadays. Specifically, modern computers have embraced multicore architectures due to the difficulties of exploiting higher clock speeds on single-core CPUs. However, parallel programming is challenging. Determinacy race, in particular, is a common pitfall when writing task-parallel code. It can easily lead to non-deterministic behavior of the parallel program and therefore a determinacy race is often considered as a bug. Unfortunately, such bugs are hard to debug because they do not necessarily produce obvious failures in every single execution. To ease the debugging process of determinacy races in task-parallel code, this dissertation proposes several provably and practically efficient parallel race detection algorithms. Unlike prior works mostly target fork-join parallelism, we focus on less structured but important programming paradigms – pipeline parallelism and futures. In addition, we build an efficient runtime system for scheduling futures, which is not only a facility to study the race detection problem for futures but also useful in practice. Finally, this dissertation investigates mechanisms that optimize the access history of race detectors, which provides significant additional boost to the performance

Efficiency Improvements in the Quality Assurance Process for Data Races

As the usage of concurrency in software has gained importance in the last years, and is still rising, new types of defects increasingly appeared in software. One of the most prominent and critical types of such new defect types are data races. Although research resulted in an increased effectiveness of dynamic quality assurance regarding data races, the efficiency in the quality assurance process still is a factor preventing widespread practical application. First, dynamic quality assurance techniques used for the detection of data races are inefficient. Too much effort is needed for conducting dynamic quality assurance. Second, dynamic quality assurance techniques used for the analysis of reported data races are inefficient. Too much effort is needed for analyzing reported data races and identifying issues in the source code. The goal of this thesis is to enable efficiency improvements in the process of quality assurance for data races by: (1) analyzing the representation of the dynamic behavior of a system under test. The results are used to focus instrumentation of this system, resulting in a lower runtime overhead during test execution compared to a full instrumentation of this system. (2) Analyzing characteristics and preprocessing of reported data races. The results of the preprocessing are then provided to developers and quality assurance personnel, enabling an analysis and debugging process, which is more efficient than traditional analysis of data race reports. Besides dynamic data race detection, which is complemented by the solution, all steps in the process of dynamic quality assurance for data races are discussed in this thesis. The solution for analyzing UML Activities for nodes possibly executing in parallel to other nodes or themselves is based on a formal foundation using graph theory. A major problem that has been solved in this thesis was the handling of cycles within UML Activities. This thesis provides a dynamic limit for the number of cycle traversals, based on the elements of each UML Activity to be analyzed and their semantics. Formal proofs are provided with regard to the creation of directed acyclic graphs and with regard to their analysis concerning the identification of elements that may be executed in parallel to other elements. Based on an examination of the characteristics of data races and data race reports, the results of dynamic data race detection are preprocessed and the outcome of this preprocessing is presented to users for further analysis. This thesis further provides an exemplary application of the solution idea, of the results of analyzing UML Activities, and an exemplary examination of the efficiency improvement of the dynamic data race detection, which showed a reduction in the runtime overhead of 44% when using the focused instrumentation compared to full instrumentation. Finally, a controlled experiment has been set up and conducted to examine the effects of the preprocessing of reported data races on the efficiency of analyzing data race reports. The results show that the solution presented in this thesis enables efficiency improvements in the analysis of data race reports between 190% and 660% compared to using traditional approaches. Finally, opportunities for future work are shown, which may enable a broader usage of the results of this thesis and further improvements in the efficiency of quality assurance for data races.Da die Verwendung von Concurrency in Software in den letzten Jahren an Bedeutung gewonnen hat, und immer noch gewinnt, sind zunehmend neue Arten von Fehlern in Software aufgetaucht. Eine der prominentesten und kritischsten Arten solcher neuer Fehlertypen sind data races. Auch wenn die Forschung zu einer steigenden Effektivität von Verfahren der dynamischen Qualitätssicherung geführt hat, so ist die Effizienz im Prozess der Qualitätssicherung noch immer ein Faktor, der eine weitverbreitete praktische Anwendung verhindert. Zum einen wird zu viel Aufwand benötigt, um dynamische Qualitätssicherung durchzuführen. Zum anderen sind die Verfahren zur Analyse gemeldeter data races ineffizient; es wird zu viel Aufwand benötigt, um gemeldete data races zu analysieren und Probleme im Quellcode zu identifizieren. Das Ziel dieser Dissertation ist es, Effizienzsteigerungen im Qualitätssicherungsprozess für data races zu ermöglichen, durch: (1) Analyse der Repräsentation des dynamischen Verhaltens des zu testenden Systems. Mit den Ergebnissen wird die Instrumentierung dieses Systems fokussiert, so dass ein im Vergleich zur vollen Instrumentierung des Systems geringerer Mehraufwand an Laufzeit benötigt wird. (2) Analyse der Charakteristiken von und Vorverarbeitung der gemeldeten data races. Die Ergebnisse der Vorverarbeitung werden Mitarbeitenden in der Entwicklung und Qualitätssicherung präsentiert, so dass ein Analyse- und Fehlerbehebungsprozess ermöglicht wird, welcher effizienter als traditionelle Analysen gemeldeter data races ist. Mit Ausnahme der dynamischen data race Erkennung, welche durch die Lösung komplementiert wird, werden alle Schritte im Prozess der dynamischen Qualitätssicherung für data races in dieser Dissertation behandelt. Die Lösung zur Analyse von UML Aktivitäten auf Knoten, die möglicherweise parallel zu sich selbst oder anderen Knoten ausgeführt werden, basiert auf einer formalen Grundlage aus dem Bereich der Graphentheorie. Eines der Hauptprobleme, welches gelöst wurde, war die Verarbeitung von Zyklen innerhalb der UML Aktivitäten. Diese Dissertation führt ein dynamisches Limit für die Anzahl an Zyklusdurchläufen ein, welches die Elemente jeder zu analysierenden UML Aktivität sowie deren Semantiken berücksichtigt. Ebenso werden formale Beweise präsentiert in Bezug auf die Erstellung gerichteter azyklischer Graphen, sowie deren Analyse zur Identifizierung von Elementen, die parallel zu anderen Elementen ausgeführt werden können. Auf Basis einer Untersuchung von Charakteristiken von data races sowie Meldungen von data races werden die Ergebnisse der dynamischen Erkennung von data races vorverarbeitet, und das Ergebnis der Vorverarbeitung gemeldeter data races wird Benutzern zur weiteren Analyse präsentiert. Diese Dissertation umfasst weiterhin eine exemplarische Anwendung der Lösungsidee und der Analyse von UML Aktivitäten, sowie eine exemplarische Untersuchung der Effizienzsteigerung der dynamischen Erkennung von data races. Letztere zeigte eine Reduktion des Mehraufwands an Laufzeit von 44% bei fokussierter Instrumentierung im Vergleich zu voller Instrumentierung auf. Abschließend wurde ein kontrolliertes Experiment aufgesetzt und durchgeführt, um die Effekte der Vorverarbeitung gemeldeter data races auf die Effizienz der Analyse dieser gemeldeten data races zu untersuchen. Die Ergebnisse zeigen, dass die in dieser Dissertation vorgestellte Lösung verglichen mit traditionellen Ansätzen Effizienzsteigerungen in der Analyse gemeldeter data races von 190% bis zu 660% ermöglicht. Abschließend werden Möglichkeiten für zukünftige Arbeiten vorgestellt, welche eine breitere Anwendung der Ergebnisse dieser Dissertation ebenso wie weitere Effizienzsteigerungen im Qualitätssicherungsprozess für data races ermöglichen können

Computer Aided Verification

This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

Analysis of Bulk Power System Resilience Using Vulnerability Graph

Critical infrastructure such as a Bulk Power System (BPS) should have some quantifiable measure of resiliency and definite rule-sets to achieve a certain resilience value. Industrial Control System (ICS) and Supervisory Control and Data Acquisition (SCADA) networks are integral parts of BPS. BPS or ICS are themselves not vulnerable because of their proprietary technology, but when the control network and the corporate network need to have communications for performance measurements and reporting, the ICS or BPS become vulnerable to cyber-attacks. Thus, a systematic way of quantifying resiliency and identifying crucial nodes in the network is critical for addressing the cyber resiliency measurement process. This can help security analysts and power system operators in the decision-making process. This thesis focuses on the resilience analysis of BPS and proposes a ranking algorithm to identify critical nodes in the network. Although there are some ranking algorithms already in place, but they lack comprehensive inclusion of the factors that are critical in the cyber domain. This thesis has analyzed a range of factors which are critical from the point of view of cyber-attacks and come up with a MADM (Multi-Attribute Decision Making) based ranking method. The node ranking process will not only help improve the resilience but also facilitate hardening the network from vulnerabilities and threats. The proposed method is called MVNRank which stands for Multiple Vulnerability Node Rank. MVNRank algorithm takes into account the asset value of the hosts, the exploitability and impact scores of vulnerabilities as quantified by CVSS (Common Vulnerability Scoring System). It also considers the total number of vulnerabilities and severity level of each vulnerability, degree centrality of the nodes in vulnerability graph and the attacker’s distance from the target node. We are using a multi-layered directed acyclic graph (DAG) model and ranking the critical nodes in the corporate and control network which falls in the paths to the target ICS. We don\u27t rank the ICS nodes but use them to calculate the potential power loss capability of the control center nodes using the assumed ICS connectivity to BPS. Unlike most of the works, we have considered multiple vulnerabilities for each node in the network while generating the rank by using a weighted average method. The resilience computation is highly time consuming as it considers all the possible attack paths from the source to the target node which increases in a multiplicative manner based on the number of nodes and vulnerabilities. Thus, one of the goals of this thesis is to reduce the simulation time to compute resilience which is achieved as illustrated in the simulation results

Computer Aided Verification

This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

Trustworthiness in Mobile Cyber Physical Systems

Computing and communication capabilities are increasingly embedded in diverse objects and structures in the physical environment. They will link the ‘cyberworld’ of computing and communications with the physical world. These applications are called cyber physical systems (CPS). Obviously, the increased involvement of real-world entities leads to a greater demand for trustworthy systems. Hence, we use "system trustworthiness" here, which can guarantee continuous service in the presence of internal errors or external attacks. Mobile CPS (MCPS) is a prominent subcategory of CPS in which the physical component has no permanent location. Mobile Internet devices already provide ubiquitous platforms for building novel MCPS applications. The objective of this Special Issue is to contribute to research in modern/future trustworthy MCPS, including design, modeling, simulation, dependability, and so on. It is imperative to address the issues which are critical to their mobility, report significant advances in the underlying science, and discuss the challenges of development and implementation in various applications of MCPS

Transcript assembly and abundance estimation with high-throughput RNA sequencing

We present algorithms and statistical methods for the reconstruction and abundance estimation of transcript sequences from high throughput RNA sequencing ("RNA-Seq"). We evaluate these approaches through large-scale experiments of a well studied model of muscle development. We begin with an overview of sequencing assays and outline why the short read alignment problem is fundamental to the analysis of these assays. We then describe two approaches to the contiguous alignment problem, one of which uses massively parallel graphics hardware to accelerate alignment, and one of which exploits an indexing scheme based on the Burrows-Wheeler transform. We then turn to the spliced alignment problem, which is fundamental to RNA-Seq, and present an algorithm, TopHat. TopHat is the first algorithm that can align the reads from an entire RNA-Seq experiment to a large genome without the aid of reference gene models. In the second part of the thesis, we present the first comparative RNA-Seq as- sembly algorithm, Cufflinks, which is adapted from a constructive proof of Dilworth's Theorem, a classic result in combinatorics. We evaluate Cufflinks by assembling the transcriptome from a time course RNA-Seq experiment of developing skeletal muscle cells. The assembly contains 13,689 known transcripts and 3,724 novel ones. Of the novel transcripts, 62% were strongly supported by earlier sequencing experiments or by homologous transcripts in other organisms. We further validated interesting genes with isoform-specific RT-PCR. We then present a statistical model for RNA-Seq included in Cufflinks and with which we estimate abundances of transcripts from RNA-seq data. Simulation studies demonstrate that the model is highly accurate. We apply this model to the muscle data, and track the abundances of individual isoforms over development. Finally, we present significance tests for changes in relative and absolute abundances between time points, which we employ to uncover differential expression and differential regulation. By testing for relative abundance changes within and between transcripts sharing a transcription start site, we find significant shifts in the rates of alternative splicing and promoter preference in hundreds of genes, including those believed to regulate muscle development