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The “HPI Future SOC Lab” is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners.
The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies.
This technical report presents results of research projects executed in 2017. Selected projects have presented their results on April 25th and November 15th 2017 at the Future SOC Lab Day events.
HPI Future SOC Lab
(2015)
Das Future SOC Lab am HPI ist eine Kooperation des Hasso-Plattner-Instituts mit verschiedenen Industriepartnern. Seine Aufgabe ist die Ermöglichung und Förderung des Austausches zwischen Forschungsgemeinschaft und Industrie.
Am Lab wird interessierten Wissenschaftlern eine Infrastruktur von neuester Hard- und Software kostenfrei für Forschungszwecke zur Verfügung gestellt. Dazu zählen teilweise noch nicht am Markt verfügbare Technologien, die im normalen Hochschulbereich in der Regel nicht zu finanzieren wären, bspw. Server mit bis zu 64 Cores und 2 TB Hauptspeicher. Diese Angebote richten sich insbesondere an Wissenschaftler in den Gebieten Informatik und Wirtschaftsinformatik. Einige der Schwerpunkte sind Cloud Computing, Parallelisierung und In-Memory Technologien.
In diesem Technischen Bericht werden die Ergebnisse der Forschungsprojekte des Jahres 2015 vorgestellt. Ausgewählte Projekte stellten ihre Ergebnisse am 15. April 2015 und 4. November 2015 im Rahmen der Future SOC Lab Tag Veranstaltungen vor.
1. Applikationen für weitverteiltes Rechnen Dennis Klemann, Lars Schmidt-Bielicke, Philipp Seuring 2. Das Globus-Toolkit Dietmar Bremser, Alexis Krepp, Tobias Rausch 3. Open Grid Services Architecture Lars Trieloff 4. Condor, Condor-G, Classad Stefan Henze, Kai Köhne 5. The Cactus Framework Thomas Hille, Martin Karlsch 6. High Performance Scheduler mit Maui/PBS Ole Weidner, Jörg Schummer, Benedikt Meuthrath 7. Bandbreiten-Monitoring mit NWS Alexander Ritter, Gregor Höfert 8. The Paradyn Parallel Performance Measurement Tool Jens Ulferts, Christian Liesegang 9. Grid-Applikationen in der Praxis Steffen Bach, Michael Blume, Helge Issel
Detection of the QRS complex is a long-standing topic in the context of electrocardiography and many algorithms build upon the knowledge of the QRS positions. Although the first solutions to this problem were proposed in the 1970s and 1980s, there is still potential for improvements. Advancements in neural network technology made in recent years also lead to the emergence of enhanced QRS detectors based on artificial neural networks. In this work, we propose a method for assessing the certainty that is in each of the detected QRS complexes, i.e. how confident the QRS detector is that there is, in fact, a QRS complex in the position where it was detected. We further show how this metric can be utilised to distinguish correctly detected QRS complexes from false detections.
Bridging the Gap
(2019)
The recent restructuring of the electricity grid (i.e., smart grid) introduces a number of challenges for today's large-scale computing systems. To operate reliable and efficient, computing systems must adhere not only to technical limits (i.e., thermal constraints) but they must also reduce operating costs, for example, by increasing their energy efficiency. Efforts to improve the energy efficiency, however, are often hampered by inflexible software components that hardly adapt to underlying hardware characteristics. In this paper, we propose an approach to bridge the gap between inflexible software and heterogeneous hardware architectures. Our proposal introduces adaptive software components that dynamically adapt to heterogeneous processing units (i.e., accelerators) during runtime to improve the energy efficiency of computing systems.
Cost models play an important role for the efficient implementation of software systems. These models can be embedded in operating systems and execution environments to optimize execution at run time. Even though non-uniform memory access (NUMA) architectures are dominating today's server landscape, there is still a lack of parallel cost models that represent NUMA system sufficiently. Therefore, the existing NUMA models are analyzed, and a two-step performance assessment strategy is proposed that incorporates low-level hardware counters as performance indicators. To support the two-step strategy, multiple tools are developed, all accumulating and enriching specific hardware event counter information, to explore, measure, and visualize these low-overhead performance indicators. The tools are showcased and discussed alongside specific experiments in the realm of performance assessment.
Inhaltsverzeichnis 1 Einführung 2 Aspektorientierte Programmierung 2.1 Ein System als Menge von Eigenschaften 2.2 Aspekte 2.3 Aspektweber 2.4 Vorteile Aspektorientierter Programmierung 2.5 Kategorisierung der Techniken und Werkzeuge f ¨ ur Aspektorientierte Programmierung 3 Techniken und Werkzeuge zur Analyse Aspektorientierter Softwareprogramme 3.1 Virtual Source File 3.2 FEAT 3.3 JQuery 3.4 Aspect Mining Tool 4 Techniken und Werkzeuge zum Entwurf Aspektorientierter Softwareprogramme 4.1 Concern Space Modeling Schema 4.2 Modellierung von Aspekten mit UML 4.3 CoCompose 4.4 Codagen Architect 5 Techniken und Werkzeuge zur Implementierung Aspektorientierter Softwareprogramme 5.1 Statische Aspektweber 5.2 Dynamische Aspektweber 6 Zusammenfassung