Schedules for Dynamic Bidirectional Simulations on Parallel Computers

For adjoint calculations, parameter estimation, and similar purposes one may need to reverse the execution of a computer program. The simplest option is to record a complete execution log and then to read it backwards. This requires massive amounts of storage. Instead one may generate the execution log piecewise by restarting the ``forward'' calculation repeatedly from suitably placed checkpoints. This thesis extends the theoretical results of the parallel reversal schedules. First a algorithm was constructed which carries out the ``forward'' calculation and distributes checkpoints in a way, such that the reversal calculation can be started at any time. This approach provides adaptive parallel reversal schedules for simulations where the number of time steps is not known a-priori. The number of checkpoints and processors used is optimal at any time. Further, an algorithm was described which makes is possible to restart the initial computer program during the program reversal. Again, this can be done without any additional computation at any time. Hence, optimal parallel reversal schedules for the bidirectional simulation are provided by this thesis.Bei der Berechnung von Adjungierten, zum Debuggen und für ähnliche Anwendungen kann man die Umkehr der entsprechenden Programmauswertung verwenden. Der einfachste Ansatz, nämlich das Erstellen einer kompletten Mitschrift der Vorwärtsrechnung, welche anschließend rückwärts gelesen wird, verursacht einen enormen Speicherplatzbedarf. Als Alternative dazu kann man die Mitschrift auch stückweise erzeugen, indem die Programmauswertung von passend gewählten Checkpoints wiederholt gestartet wird. In dieser Arbeit wird die Theorie der optimalen parallelen Umkehrschemata erweitert. Zum einen erfolgt die Konstruktion von adaptiven parallelen Umkehrschemata. Dafür wird ein Algorithmus beschrieben, der es durch die Nutzung von mehreren Prozessen ermöglicht, Checkpoints so zu verteilen, daß die Umkehrung des Programmes jederzeit ohne Zeitverlust erfolgen kann. Hierbei bleibt die Zahl der verwendeten Checkpoints und Prozesse innerhalb der bekannten Optimalitätsgrenzen. Zum anderen konnte für die adaptiven parallelen Umkehrschemata ein Algorithmus entwickelt werden, welcher ein Restart der eigentlichen Programmauswertung basierend auf der laufenden Programmumkehr erlaubt. Dieser Restart kann wieder jederzeit ohne Zeitverlust erfolgen und die entstehenden Checkpointverteilung erfüllen wieder sowohl Optimalitäts- als auch die Adaptivitätskriterien. Zusammenfassend wurden damit in dieser Arbeit Schemata konstruiert, die bidirektionale Simulationen ermöglichen

Detector Simulation Challenges for Future Accelerator Experiments

Detector simulation is a key component for studies on prospective future high-energy colliders, the design, optimization, testing and operation of particle physics experiments, and the analysis of the data collected to perform physics measurements. This review starts from the current state of the art technology applied to detector simulation in high-energy physics and elaborates on the evolution of software tools developed to address the challenges posed by future accelerator programs beyond the HL-LHC era, into the 2030–2050 period. New accelerator, detector, and computing technologies set the stage for an exercise in how detector simulation will serve the needs of the high-energy physics programs of the mid 21st century, and its potential impact on other research domains

Extending Capability and Implementing a Web Interface for the XALT Software Monitoring Tool

As high performance computing centers evolve in terms of hardware, software, and user-base, the act of monitoring and managing such systems requires specialized tools. The tool discussed in this thesis is XALT, which is a collaborative effort between the National Institute for Computational Sciences and Texas Advanced Computing Center. XALT is designed to track link-time and job level information for applications that are compiled and executed on any Linux cluster, workstation, or high-end supercomputer. The key objectives of this work are to extend the existing functionality of XALT and implement a real-time web portal to easily visualize the tracked data. A prototype is developed to track function calls resolved by external libraries which helps software management. The web portal generates reports and metrics which would improve efficiency and effectiveness for an extensive community of stakeholders including users, support organizations, and development teams. In addition, we discuss use cases of interest to center support staff and researchers on identifying users based on given counters and generating provenance reports. This work details the opportunity and challenges to further push XALT towards becoming a complete package

1. GI FG SIDAR Graduierten-Workshop über Reaktive Sicherheit (SPRING)

SPRING ist eine wissenschaftliche Veranstaltung im Bereich der Reaktiven Sicherheit, die Nachwuchswissenschaftlern die Möglichkeit bietet, Ergebnisse eigener Arbeiten zu präsentieren und dabei Kontakte über die eigene Universität hinaus zu knüpfen. SPRING wurde am 12. Juli 2006 in Berlin im Zusammenhang mit der internationalen SIDAR-Konferenz "Detection of Intrusions and Malware & Vulnerability Assessment" (DIMVA) in den Räumen der Berlin-Brandenburgischen Akademie der Wissenschaften veranstaltet Die Arbeiten deckten ein breites Spektrum ab, von noch laufenden Projekten, die ggf. erstmals einem breiteren Publikum vorgestellt werden, bis zu abgeschlossenen Forschungsarbeiten, die zeitnah auch auf Konferenzen präsentiert wurden bzw. werden sollen oder einen Schwerpunkt der eigenen Diplomarbeit oder Dissertation bilden. Die zugehörigen Abstracts sind in diesem technischen Bericht zusammengefaßt. In dieser Ausgabe finden sich Beiträge zur den folgenden Themen: Verwundbarkeiten und Malware, Incident Management und Forensik, sowie zu verschiedenen Spezialthemen der Intrusion Detection: Realisierungsaspekte, Modellgenerierung und -validierung, sowie neue Technologien

Adaptive Response System for Distributed Denial-of-Service Attacks

The continued prevalence and severe damaging effects of the Distributed Denial of Service (DDoS) attacks in today’s Internet raise growing security concerns and call for an immediate response to come up with better solutions to tackle DDoS attacks. The current DDoS prevention mechanisms are usually inflexible and determined attackers with knowledge of these mechanisms, could work around them. Most existing detection and response mechanisms are standalone systems which do not rely on adaptive updates to mitigate attacks. As different responses vary in their “leniency” in treating detected attack traffic, there is a need for an Adaptive Response System. We designed and implemented our DDoS Adaptive ResponsE (DARE) System, which is a distributed DDoS mitigation system capable of executing appropriate detection and mitigation responses automatically and adaptively according to the attacks. It supports easy integrations for both signature-based and anomaly-based detection modules. Additionally, the design of DARE’s individual components takes into consideration the strengths and weaknesses of existing defence mechanisms, and the characteristics and possible future mutations of DDoS attacks. These components consist of an Enhanced TCP SYN Attack Detector and Bloom-based Filter, a DDoS Flooding Attack Detector and Flow Identifier, and a Non Intrusive IP Traceback mechanism. The components work together interactively to adapt the detections and responses in accordance to the attack types. Experiments conducted on DARE show that the attack detection and mitigation are successfully completed within seconds, with about 60% to 86% of the attack traffic being dropped, while availability for legitimate and new legitimate requests is maintained. DARE is able to detect and trigger appropriate responses in accordance to the attacks being launched with high accuracy, effectiveness and efficiency. We also designed and implemented a Traffic Redirection Attack Protection System (TRAPS), a stand-alone DDoS attack detection and mitigation system for IPv6 networks. In TRAPS, the victim under attack verifies the authenticity of the source by performing virtual relocations to differentiate the legitimate traffic from the attack traffic. TRAPS requires minimal deployment effort and does not require modifications to the Internet infrastructure due to its incorporation of the Mobile IPv6 protocol. Experiments to test the feasibility of TRAPS were carried out in a testbed environment to verify that it would work with the existing Mobile IPv6 implementation. It was observed that the operations of each module were functioning correctly and TRAPS was able to successfully mitigate an attack launched with spoofed source IP addresses

Agent-Based Cloud Resource Management for Secure Cloud Infrastructures

The cloud offers clear benefits for computations as well as for storage for diverse application areas. Security concerns are by far the greatest barriers to the wider uptake of cloud computing, particularly for privacy-sensitive applications. The aim of this article is to propose an approach for establishing trust between users and providers of cloud infrastructures (IaaS model) based on certified trusted agents. Such approach would remove barriers that prevent security sensitive applications being moved to the cloud. The core technology encompasses a secure agent platform for providing the execution environment for agents and the secure attested software base which ensures the integrity of the host platform. In this article we describe the motivation, concept, design and initial implementation of these technologies

Jet Momentum Resolution for the CMS Experiment and Distributed Data Caching Strategies

Accurately measured jets are mandatory for precision measurements of the Standard Model of particle physics as well as for searches for new physics. The increased instantaneous luminosity and center-of-mass energy at LHC Run 2 pose challenges for pileup mitigation and the measurement of jet characteristics. This thesis concentrates on using Z + jets events to calibrate the energy scale of jets recorded by the CMS detector in 2018. Furthermore, it proposes a new procedure for determining the jet momentum resolution using Z + jets events. This procedure is expected to allow cross-checking complementary measurement approaches and increasing the accuracy of the jet momentum resolution at the CMS experiment. Data-intensive end-user analyses in High Energy Physics such as the presented calibration of jets put enormous challenges on the computing infrastructure since requiring high data throughput. Besides the particle physics analysis, this thesis also focuses on accelerating data processing within a distributed computing infrastructure via a coordinated distributed caching approach. Coordinated placement of critical data within distributed caches and matching workflows to the most suitable host in terms of cached data allows for optimizing processing efficiency. Improving the processing of data-intensive workflows aims at shortening turnaround cycles and thus deriving physics results, e.g. the jet calibration results, faster

Fred: A GPU-accelerated fast-Monte Carlo code for rapid treatment plan recalculation in ion beam therapy

Ion beam therapy is a rapidly growing technique for tumor radiation therapy. Ions allow for a high dose deposition in the tumor region, while sparing the surrounding healthy tissue. For this reason, the highest possible accuracy in the calculation of dose and its spatial distribution is required in treatment planning. On one hand, commonly used treatment planning software solutions adopt a simplified beam-body interaction model by remapping pre-calculated dose distributions into a 3D water-equivalent representation of the patient morphology. On the other hand, Monte Carlo (MC) simulations, which explicitly take into account all the details in the interaction of particles with human tissues, are considered to be the most reliable tool to address the complexity of mixed field irradiation in a heterogeneous environment. However, full MC calculations are not routinely used in clinical practice because they typically demand substantial computational resources. Therefore MC simulations are usually only used to check treatment plans for a restricted number of difficult cases. The advent of general-purpose programming GPU cards prompted the development of trimmed-down MC-based dose engines which can significantly reduce the time needed to recalculate a treatment plan with respect to standard MC codes in CPU hardware. In this work, we report on the development of fred, a new MC simulation platform for treatment planning in ion beam therapy. The code can transport particles through a 3D voxel grid using a class II MC algorithm. Both primary and secondary particles are tracked and their energy deposition is scored along the trajectory. Effective models for particle-medium interaction have been implemented, balancing accuracy in dose deposition with computational cost. Currently, the most refined module is the transport of proton beams in water: single pencil beam dose-depth distributions obtained with fred agree with those produced by standard MC codes within 1-2% of the Bragg peak in the therapeutic energy range. A comparison with measurements taken at the CNAO treatment center shows that the lateral dose tails are reproduced within 2% in the field size factor test up to 20 cm. The tracing kernel can run on GPU hardware, achieving 10 million primary on a single card. This performance allows one to recalculate a proton treatment plan at 1% of the total particles in just a few minutes

Detector simulation challenges for future accelerator experiments

Detector simulation is a key component for studies on prospective future high-energy colliders, the design, optimization, testing and operation of particle physics experiments, and the analysis of the data collected to perform physics measurements. This review starts from the current state of the art technology applied to detector simulation in high-energy physics and elaborates on the evolution of software tools developed to address the challenges posed by future accelerator programs beyond the HL-LHC era, into the 2030–2050 period. New accelerator, detector, and computing technologies set the stage for an exercise in how detector simulation will serve the needs of the high-energy physics programs of the mid 21st century, and its potential impact on other research domains

Load classification and appliance fingerprinting for residential load monitoring system

Previous work on residential load monitoring has attempted to address different requirements including the systematic collection of information about electric power consumption for load research purpose, the provision of a detailed consumption report to facilitate energy conservation practices and the monitoring of critical loads for fault diagnostics. This work focuses on developing methods for appliance fingerprinting that is foreseen to be an integral part of an automatic residential load monitoring system. Various approaches outlined in previous research form the basis for the concepts developed in this thesis. In addition, an extensive series of measurement work was performed on several household appliances in order to acquire the necessary operation data for building the technique and also to explore the extent up to which residential loads can be categorized into distinct groups. The fingerprinting process proposed in this work employs three main phases: feature extraction of electrical attributes, event detection and pattern recognition. Test results obtained at different stages of the work using the measurement data are also discussed in detail. Such studies are necessary to enable utilities to manage their networks reliably and efficiently, and also to encourage the active participation of consumers in energy conservation programs