Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

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Collective Computation in Object-based Parallel Programming Languages

Bal, H.E. [Promotor

Partial aggregation for collective communication in distributed memory machines

High Performance Computing (HPC) systems interconnect a large number of Processing Elements (PEs) in high-bandwidth networks to simulate complex scientific problems. The increasing scale of HPC systems poses great challenges on algorithm designers. As the average distance between PEs increases, data movement across hierarchical memory subsystems introduces high latency. Minimizing latency is particularly challenging in collective communications, where many PEs may interact in complex communication patterns. Although collective communications can be optimized for network-level parallelism, occasional synchronization delays due to dependencies in the communication pattern degrade application performance. To reduce the performance impact of communication and synchronization costs, parallel algorithms are designed with sophisticated latency hiding techniques. The principle is to interleave computation with asynchronous communication, which increases the overall occupancy of compute cores. However, collective communication primitives abstract parallelism which limits the integration of latency hiding techniques. Approaches to work around these limitations either modify the algorithmic structure of application codes, or replace collective primitives with verbose low-level communication calls. While these approaches give fine-grained control for latency hiding, implementing collective communication algorithms is challenging and requires expertise knowledge about HPC network topologies. A collective communication pattern is commonly described as a Directed Acyclic Graph (DAG) where a set of PEs, represented as vertices, resolve data dependencies through communication along the edges. Our approach improves latency hiding in collective communication through partial aggregation. Based on mathematical rules of binary operations and homomorphism, we expose data parallelism in a respective DAG to overlap computation with communication. The proposed concepts are implemented and evaluated with a subset of collective primitives in the Message Passing Interface (MPI), an established communication standard in scientific computing. An experimental analysis with communication-bound microbenchmarks shows considerable performance benefits for the evaluated collective primitives. A detailed case study with a large-scale distributed sort algorithm demonstrates, how partial aggregation significantly improves performance in data-intensive scenarios. Besides better latency hiding capabilities with collective communication primitives, our approach enables further optimizations of their implementations within MPI libraries. The vast amount of asynchronous programming models, which are actively studied in the HPC community, benefit from partial aggregation in collective communication patterns. Future work can utilize partial aggregation to improve the interaction of MPI collectives with acclerator architectures, and to design more efficient communication algorithms

Realtime ray tracing on current CPU architectures

In computer graphics, ray tracing has become a powerful tool for generating realistically looking images. Even though ray tracing offers high flexibility, a logarithmic scalability in scene complexity, and is known to be efficiently parallelizable, its demand for compute power has in the past lead to its limitation to high-quality off-line rendering. This thesis focuses on the question of how realtime ray tracing can be realized on current processor architectures. To this end, it provides a detailed analysis of the weaknesses and strengths of current processor architectures, for the purpose of allowing for highly optimized implementation. The combination of processor-specific optimizations with algorithms that exploit the coherence of ray tracing, makes it possible to achieve realtime performance on a single CPU. Besides the optimization of the ray tracing algorithm itself, this thesis focuses on the efficient building of spatial index structures. By building these structures from scratch for every frame, interactive ray tracing of fully dynamic scenes becomes possible. Moreover, a parallelization framework for ray tracing is discussed that efficiently exploits the compute power of a cluster of commodity PCs. Finally, a global illumination algorithm is proposed that efficiently combines optimized ray tracing and the parallelization framework. The combination makes it possible to compute complete global illumination at interactive frame rates

Realtime ray tracing and interactive global illumination

One of the most sought-for goals in computer graphics is to generate "realism in real time". i.e. the generation of realistically looking images at realtime frame rates. Today, virtually all approaches towards realtime rendering use graphics hardware, which is based almost exclusively on triangle rasterization. Unfortunately, though this technology has seen tremendous progress over the last few years, for many applications it is currently reaching its limits in both model complexity, supported features, and achievable realism. An alternative to triangle rasterizations is the ray tracing algorithm, which is well-known for its higher flexibility, its generally higher achievable realism, and its superior scalability in both model size and compute power. However, ray tracing is also computationally demanding and thus so far is used almost exclusively for high-quality offline rendering tasks. This dissertation focuses on the question why ray tracing is likely to soon play a larger role for interactive applications, and how this scenario can be reached. To this end, we discuss the RTRT/OpenRT realtime ray tracing system, a software based ray tracing system that achieves interactive to realtime frame rates on todays commodity CPUs. In particular, we discuss the overall system design, the efficient implementation of the core ray tracing algorithms, techniques for handling dynamic scenes, an efficient parallelization framework, and an OpenGL-like low-level API. Taken together, these techniques form a complete realtime rendering engine that supports massively complex scenes, highley realistic and physically correct shading, and even physically based lighting simulation at interactive rates. In the last part of this thesis we then discuss the implications and potential of realtime ray tracing on global illumination, and how the availability of this new technology can be leveraged to finally achieve interactive global illumination - the physically correct simulation of light transport at interactive rates.Eines der wichtigsten Ziele der Computer-Graphik ist die Generierung von "Realismus in Echtzeit\u27; — die Erzeugung von realistisch wirkenden, computer- generierten Bildern in Echtzeit. Heutige Echtzeit-Graphikanwendungen werden derzeit zum überwiegenden Teil mit schneller Graphik-Hardware realisiert, welche zum aktuellen Stand der Technik fast ausschliesslich auf dem Dreiecksrasterisierungsalgorithmus basiert. Obwohl diese Rasterisierungstechnologie in den letzten Jahren zunehmend beeindruckende Fortschritte gemacht hat, stößt sie heutzutage zusehends an ihre Grenzen, speziell im Hinblick auf Modellkomplexität, unterstützte Beleuchtungseffekte, und erreichbaren Realismus. Eine Alternative zur Dreiecksrasterisierung ist das "Ray-Tracing\u27; (Stahl-Rückverfolgung), welches weithin bekannt ist für seine höhere Flexibilität, seinen im Großen und Ganzen höheren erreichbaren Realismus, und seine bessere Skalierbarkeit sowohl in Szenengröße als auch in Rechner-Kapazitäten. Allerdings ist Ray-Tracing ebenso bekannt für seinen hohen Rechenbedarf, und wird daher heutzutage fast ausschließlich für die hochqualitative, nichtinteraktive Bildsynthese benutzt. Diese Dissertation behandelt die Gründe warum Ray-Tracing in näherer Zukunft voraussichtlich eine größere Rolle für interaktive Graphikanwendungen spielen wird, und untersucht, wie dieses Szenario des Echtzeit Ray-Tracing erreicht werden kann. Hierfür stellen wir das RTRT/OpenRT Echtzeit Ray-Tracing System vor, ein software-basiertes Ray-Tracing System, welches es erlaubt, interaktive Performanz auf heutigen Standard-PC-Prozessoren zu erreichen. Speziell diskutieren wir das grundlegende System-Design, die effiziente Implementierung der Kern-Algorithmen, Techniken zur Unterstützung von dynamischen Szenen, ein effizientes Parallelisierungs-Framework, und eine OpenGL-ähnliche Anwendungsschnittstelle. In ihrer Gesamtheit formen diese Techniken ein komplettes Echtzeit-Rendering-System, welches es erlaubt, extrem komplexe Szenen, hochgradig realistische und physikalisch korrekte Effekte, und sogar physikalisch-basierte Beleuchtungssimulation interaktiv zu berechnen. Im letzten Teil der Dissertation behandeln wir dann die Implikationen und das Potential, welches Echtzeit Ray-Tracing für die Globale Beleuchtungssimulation bietet, und wie die Verfügbarkeit dieser neuen Technologie benutzt werden kann, um letztendlich auch Globale Belechtung — die physikalisch korrekte Simulation des Lichttransports — interaktiv zu berechnen

A Process Model for the Integrated Reasoning about Quantitative IT Infrastructure Attributes

IT infrastructures can be quantitatively described by attributes, like performance or energy efficiency. Ever-changing user demands and economic attempts require varying short-term and long-term decisions regarding the alignment of an IT infrastructure and particularly its attributes to this dynamic surrounding. Potentially conflicting attribute goals and the central role of IT infrastructures presuppose decision making based upon reasoning, the process of forming inferences from facts or premises. The focus on specific IT infrastructure parts or a fixed (small) attribute set disqualify existing reasoning approaches for this intent, as they neither cover the (complex) interplay of all IT infrastructure components simultaneously, nor do they address inter- and intra-attribute correlations sufficiently. This thesis presents a process model for the integrated reasoning about quantitative IT infrastructure attributes. The process model’s main idea is to formalize the compilation of an individual reasoning function, a mathematical mapping of parametric influencing factors and modifications on an attribute vector. Compilation bases upon model integration to benefit from the multitude of existing specialized, elaborated, and well-established attribute models. The achieved reasoning function consumes an individual tuple of IT infrastructure components, attributes, and external influencing factors to expose a broad applicability. The process model formalizes a reasoning intent in three phases. First, reasoning goals and parameters are collected in a reasoning suite, and formalized in a reasoning function skeleton. Second, the skeleton is iteratively refined, guided by the reasoning suite. Third, the achieved reasoning function is employed for What-if analyses, optimization, or descriptive statistics to conduct the concrete reasoning. The process model provides five template classes that collectively formalize all phases in order to foster reproducibility and to reduce error-proneness. Process model validation is threefold. A controlled experiment reasons about a Raspberry Pi cluster’s performance and energy efficiency to illustrate feasibility. Besides, a requirements analysis on a world-class supercomputer and on the European-wide execution of hydro meteorology simulations as well as a related work examination disclose the process model’s level of innovation. Potential future work employs prepared automation capabilities, integrates human factors, and uses reasoning results for the automatic generation of modification recommendations.IT-Infrastrukturen können mit Attributen, wie Leistung und Energieeffizienz, quantitativ beschrieben werden. Nutzungsbedarfsänderungen und ökonomische Bestrebungen erfordern Kurz- und Langfristentscheidungen zur Anpassung einer IT-Infrastruktur und insbesondere ihre Attribute an dieses dynamische Umfeld. Potentielle Attribut-Zielkonflikte sowie die zentrale Rolle von IT-Infrastrukturen erfordern eine Entscheidungsfindung mittels Reasoning, einem Prozess, der Rückschlüsse (rein) aus Fakten und Prämissen zieht. Die Fokussierung auf spezifische Teile einer IT-Infrastruktur sowie die Beschränkung auf (sehr) wenige Attribute disqualifizieren bestehende Reasoning-Ansätze für dieses Vorhaben, da sie weder das komplexe Zusammenspiel von IT-Infrastruktur-Komponenten, noch Abhängigkeiten zwischen und innerhalb einzelner Attribute ausreichend berücksichtigen können. Diese Arbeit präsentiert ein Prozessmodell für das integrierte Reasoning über quantitative IT-Infrastruktur-Attribute. Die grundlegende Idee des Prozessmodells ist die Herleitung einer individuellen Reasoning-Funktion, einer mathematischen Abbildung von Einfluss- und Modifikationsparametern auf einen Attributvektor. Die Herleitung basiert auf der Integration bestehender (Attribut-)Modelle, um von deren Spezialisierung, Reife und Verbreitung profitieren zu können. Die erzielte Reasoning-Funktion verarbeitet ein individuelles Tupel aus IT-Infrastruktur-Komponenten, Attributen und externen Einflussfaktoren, um eine breite Anwendbarkeit zu gewährleisten. Das Prozessmodell formalisiert ein Reasoning-Vorhaben in drei Phasen. Zunächst werden die Reasoning-Ziele und -Parameter in einer Reasoning-Suite gesammelt und in einem Reasoning-Funktions-Gerüst formalisiert. Anschließend wird das Gerüst entsprechend den Vorgaben der Reasoning-Suite iterativ verfeinert. Abschließend wird die hergeleitete Reasoning-Funktion verwendet, um mittels “What-if”–Analysen, Optimierungsverfahren oder deskriptiver Statistik das Reasoning durchzuführen. Das Prozessmodell enthält fünf Template-Klassen, die den Prozess formalisieren, um Reproduzierbarkeit zu gewährleisten und Fehleranfälligkeit zu reduzieren. Das Prozessmodell wird auf drei Arten validiert. Ein kontrolliertes Experiment zeigt die Durchführbarkeit des Prozessmodells anhand des Reasonings zur Leistung und Energieeffizienz eines Raspberry Pi Clusters. Eine Anforderungsanalyse an einem Superrechner und an der europaweiten Ausführung von Hydro-Meteorologie-Modellen erläutert gemeinsam mit der Betrachtung verwandter Arbeiten den Innovationsgrad des Prozessmodells. Potentielle Erweiterungen nutzen die vorbereiteten Automatisierungsansätze, integrieren menschliche Faktoren, und generieren Modifikationsempfehlungen basierend auf Reasoning-Ergebnissen