Self adaptive computation of the breakdown voltage of planar pn-junctions with multistep field plates

The breakdown voltage highly depends on the electric field in the depletion area whose computation is the most time consuming part of the simulation. We present a self adaptive Finite Element Method which reduces dramatically the required computation time compared to usual Finite Difference Methods. A numerical example illustrates the efficiency and reliability of the algorithm

BOXES - a program to generate triangulations from a rectangular domain description

BOXES computes a triangulation from a 2D domain description which consists of an arbitrary set of rectangles. Each rectangle may have attributes to control the triangulating process, define subdomain classes, or specify boundary conditions. The output of the program can be used as a coarse grid for KASKADE or one of its variants

On adaptive grid refinement in the presence of internal or boundary layers

We propose an anisotropic refinement strategy which is specially designed for the efficient numerical resulution of internal and boundary layers. This strategy is based on the directed refinement of single triangles together with adaptive multilevel grid orientation. Compared to usual methods, the new anisotropic refinement ends up in more stable and more accurate solutions at much less computational cost. This is demonstrated by several numerical examples

Slice-Level Trading of Quality and Performance in Decoding H.264 Video: Slice-basiertes Abwägen zwischen Qualität und Leistung beim Dekodieren von H.264-Video

When a demanding video decoding task requires more CPU resources then available, playback degrades ungracefully today: The decoder skips frames selected arbitrarily or by simple heuristics, which is noticed by the viewer as jerky motion in the good case or as images completely breaking up in the bad case. The latter can happen due to missing reference frames. This thesis provides a way to schedule individual decoding tasks based on a cost for performance trade. Therefore, I will present a way to preprocess a video, generating estimates for the cost in terms of execution time and the performance in terms of perceived visual quality. The granularity of the scheduling decision is a single slice, which leads to a much more fine-grained approach than dealing with entire frames. Together with an actual scheduler implementation that uses the generated estimates, this work allows for higher perceived quality video playback in case of CPU overload.Wenn eine anspruchsvolle Video-Dekodierung mehr Prozessor-Ressourcen benötigt, als verfügbar sind, dann verschlechtert sich die Abspielqualität mit aktuellen Methoden drastisch: Willkürlich oder mit einfachen Heuristiken ausgewählten Bilder werden nicht dekodiert. Diese Auslassung nimmt der Betrachter im günstigsten Fall nur als ruckelnde Bewegung wahr, im ungünstigen Fall jedoch als komplettes Zusammenbrechen nachfolgender Bilder durch Folgefehler im Dekodierprozess. Meine Arbeit ermöglicht es, einzelne Teilaufgaben des Dekodierprozesses anhand einer Kosten-Nutzen-Analyse einzuplanen. Dafür ermittle ich die Kosten im Sinne von Rechenzeitbedarf und den Nutzen im Sinne von visueller Qualität für einzelne Slices eines H.264 Videos. Zusammen mit einer Implementierung eines Schedulers, der diese Werte nutzt, erlaubt meine Arbeit höhere vom Betrachter wahrgenommene Videoqualität bei knapper Prozessorzeit

CoRD: Converged RDMA Dataplane for High-Performance Clouds

High-performance networking is often characterized by kernel bypass which is considered mandatory in high-performance parallel and distributed applications. But kernel bypass comes at a price because it breaks the traditional OS architecture, requiring applications to use special APIs and limiting the OS control over existing network connections. We make the case, that kernel bypass is not mandatory. Rather, high-performance networking relies on multiple performance-improving techniques, with kernel bypass being the least effective. CoRD removes kernel bypass from RDMA networks, enabling efficient OS-level control over RDMA dataplane.Comment: 11 page

Practical Real-Time with Look-Ahead Scheduling

In my dissertation, I present ATLAS — the Auto-Training Look-Ahead Scheduler. ATLAS improves service to applications with regard to two non-functional properties: timeliness and overload detection. Timeliness is an important requirement to ensure user interface responsiveness and the smoothness of multimedia operations. Overload can occur when applications ask for more computation time than the machine can offer. Interactive systems have to handle overload situations dynamically at runtime. ATLAS provides timely service to applications, accessible through an easy-to-use interface. Deadlines specify timing requirements, workload metrics describe jobs. ATLAS employs machine learning to predict job execution times. Deadline misses are detected before they occur, so applications can react early.:1 Introduction 2 Anatomy of a Desktop Application 3 Real Simple Real-Time 4 Execution Time Prediction 5 System Scheduler 6 Timely Service 7 The Road Ahead Bibliography Inde

Adaptive Finite-Element-Methoden für konvektionsdominierte Randwertprobleme bei partiellen Differentialgleichungen

Ausgangspunkt bei der Behandlung konvektiv dominierter, elliptischer Probleme sind die bekannten hierarchischen Finite-Element-Methoden für den rein elliptischen Fall. Als stabile Erweiterung des Standard-Galerkin-Verfahrens wird das Stromlinien-Diffusions-Verfahren durch physikalische Überlegungen motiviert und kurz diskutiert. Anschließend zeigen wir, daß diese Methode erst in Verbindung mit einer hier erstmals vorgestellten lokalen Ausrichtung der Kanten wirksam eingesetzt werden kann. Zusammen mit einer ebenfalls neu entwickelten richtungsorientierten Verfeinerungsstrategie erhält man eine erheblich stabilere, genauere und schnellere Auflösung von Grenzschichten als mit herkömmlichen Methoden

Increased Brain Reward Responsivity to Food-Related Odors in Obesity

Objective Food odors serve as powerful stimuli signaling the food quality and energy density and direct food-specific appetite and consumption. This study explored obesity-related brain activation in response to odors related to high- or low-energy-dense foods. Methods Seventeen participants with obesity (BMI > 30 kg/m2 ; 4 males and 13 females) and twenty-one with normal weight (BMI < 25 kg/m2 ; 9 males and 12 females) underwent a functional magnetic resonance imaging scan in which they received chocolate (high-energy-dense food) and cucumber (low-energy-dense food) odor stimuli. Participants' olfactory and gustatory functions were assessed by the "Sniffin' Sticks" and "Taste Strips" tests, respectively. Results Compared with normal-weight controls, participants with obesity had lower odor sensitivity (phenylethyl alcohol) and decreased odor discrimination ability. However, participants with obesity demonstrated greater brain activation in response to chocolate compared with cucumber odors in the bilateral inferior frontal operculum and cerebellar vermis, right ventral anterior insula extending to putamen, right middle temporal gyrus, and right supramarginal areas. Conclusions The present study provides preliminary evidence that obesity is associated with heightened brain activation of the reward and flavor processing areas in response to chocolate versus cucumber odors, possibly because of the higher energy density and reinforcing value of chocolate compared with cucumber.Peer reviewe

Caladan: a distributed meta-OS for data center disaggregation

Data center resource disaggregation promises cost savings by pool-ing compute, storage and memory resources into separate, net-worked nodes. The benefits of this model are clear, but a closer lookshows that its full performance and efficiency potential cannot beeasily realized. Existing systems use CPUs pervasively to interface ar-bitrary devices with the network and to orchestrate communicationamong them, reducing the benefits of disaggregation.In this paper we presentCaladan, a novel system with a trusteduni-versal resource fabricthat interconnects all resources and efficientlyoffloads the system and application control planes to SmartNICs,freeing server CPUs to execute application logic. Caladan offersthree core services: capability-driven distributed name space, virtualdevices, and direct inter-device communications. These servicesare implemented in a trustedmeta-kernelthat executes in per-nodeSmartNICs. Low-level device drivers running on the commodity hostOS are used for setting up accelerators and I/O devices, and exposingthem to Caladan. Applications run in a distributed fashion acrossCPUs and multiple accelerators, which in turn can directly performI/O, i.e., access files, other accelerators or host services. Our dis-tributed dataflow runtime runs on top of this substrate. It orchestratesthe distributed execution, connecting disaggregated resources usingdata transfers and inter-device communication, while eliminatingthe performance bottlenecks of the traditional CPU-centric design