Traversing large graphs in realistic settings

The notion of graph traversal is of fundamental importance to solving many computational problems. In many modern applications involving graph traversal such as those arising in the domain of social networks, Internet based services, fraud detection in telephone calls etc., the underlying graph is very large and dynamically evolving. This thesis deals with the design and engineering of First Search (BFS) algorithms for massive sparse undirected graphs. Our pipelined implementations with low constant factors, together with some heuristics preserving the worst-case guarantees makes BFS viable on massive graphs. We perform an extensive set of experiments to study the effect of various graph properties such as diameter, inititraversal algorithms for such graphs. We engineer various I/O-efficient Breadth al disk layouts, tuning parameters, disk parallelism, cache-obliviousness etc. on the relative performance of these algorithms. We characterize the performance of NAND flash based storage devices, including many solid state disks. We show that despite the similarities between flash memory and RAM (fast random reads) and between flash disk and hard disk (both are block based devices), the algorithms designed in the RAM model or the external memory model do not realize the full potential of the flash memory devices. We also analyze the effect of misalignments, aging, past I/O patterns, etc. on the performance obtained on these devices. We also consider I/O-efficient BFS algorithms for the case when a hard disk and a solid state disk are used together. We present a simple algorithm which maintains the topological order of a directed acyclic graph with n nodes under an online edge insertion sequence in O(n2.75)time, independent of the number m of edges inserted. For dense DAGs, this is an improvement over the previous best result of O (min{m3/2 logn,m3/2 +n2 logn}). While our analysis holds only for the incremental setting, our algorithm itself is fully dynamic. We also present the first average-case analysis of online topological ordering algorithms. We prove an expected runtime of O (n2 polylog(n)) under insertion of the edges of a complete DAG in a random order for various incremental topological ordering algorithms.Die Traversierung von Graphen ist von fundamentaler Bedeutung für das Lösen vieler Berechnungsprobleme. Moderne Anwendungen, die auf Graphtraversierung beruhen, findet man unter anderem in sozialen Netzwerken, internetbasierten Dienstleistungen, Betrugserkennung bei Telefonanrufen. In vielen dieser lAnwendungen ist der zugrunde iegende Graph sehr gross und ändert sich kontinuierlich. Wir entwickelnmehrere I/O-effiziente Breitensuch-Algorithmen für massive, dünnbesiedelte, ungerichtete Graphen. Im Zusammenspiel mit Heuristiken zur Einhaltung von Worst-Case-Garantien, ermöglichen unsere pipeline-basierten Implementierungen die Praktikabilität von Breitensuche auf massiven Graphen. Wir führen eine Vielfalt an Experimente durch, um die Wirkung unterschiedlicher Grapheigenschaften zu untersuchen, wie z.B. Graph-Durchmesser, anfängliche Belegung der Festplatte, Tuning-Parameter, Plattenparallelismus. Wir charakterisieren die Leistung von NAND-Flash basierten Speichermedien, einschliesslich vieler solid-state Disks. Wir zeigen, dass trotz der Ähnlichkeiten von Flash-Speicher und RAM (schnelle wahlfreie Lese-Zugriffe) und von Flash-Platten und Festplatten (beide sind blockbasiert) Algorithmen, die für das RAMModell oder das Externspeicher-Modell entworfenen wurden, nicht das volle Potential der Flash-Speicher-Medien ausschöpfen. Zusätzlich analysieren wir die Wirkung von Ausrichtungsfehlern, Alterung, vorausgehenden I/O-Mustern, usw., auf die Leistung dieser Medien. Wir berücksichtigen auch I/O-effiziente Breitensuch-Algorithmen für die gleichzeitige Nutzung von Festplatten und solid-state Disks. Wir stellen einen einfachen Algorithmus vor, der beim Online-Einfügen von Kanten die topologische Ordnung von einem gerichteten, azyklischen Graphen (DAG) mit n Knoten beibehält. Dieser Algorithmus hat eine Laufzeitkomplexität von O(n2.75) unabhängig von der Anzahl m der eingefügten Kanten. Für dichte DAGs ist dies eine Verbesserung des besten, vorherigen Ergebnisses von O(min{m3/2 logn,m3/2 +n2 logn}). Während die Analyse nur im inkrementellen Szenario gütlig ist, ist unser Algorithmus vollständig dynamisch. Ferner stellen wir die erste Average-Case-Analyse von Online-Algorithmen zur Unterhaltung einer topologischen Ordnung vor. Für mehrere inkrementelle Algorithmen, welche die Kanten eines kompletten DAGs in zufälliger Reihenfolge einfügen, beweisen wir eine erwartete Laufzeit von O(n2 polylog(n))

Power models, energy models and libraries for energy-efficient concurrent data structures and algorithms

EXCESS deliverable D2.3. More information at http://www.excess-project.eu/This deliverable reports the results of the power models, energy models and librariesfor energy-efficient concurrent data structures and algorithms as available by projectmonth 30 of Work Package 2 (WP2). It reports i) the latest results of Task 2.2-2.4 onproviding programming abstractions and libraries for developing energy-efficient datastructures and algorithms and ii) the improved results of Task 2.1 on investigating andmodeling the trade-off between energy and performance of concurrent data structuresand algorithms. The work has been conducted on two main EXCESS platforms: Intelplatforms with recent Intel multicore CPUs and Movidius Myriad platforms

Algorithm Engineering for fundamental Sorting and Graph Problems

Fundamental Algorithms build a basis knowledge for every computer science undergraduate or a professional programmer. It is a set of basic techniques one can find in any (good) coursebook on algorithms and data structures. In this thesis we try to close the gap between theoretically worst-case optimal classical algorithms and the real-world circumstances one face under the assumptions imposed by the data size, limited main memory or available parallelism

Route Planning in Road Networks

We present various speedup techniques for route planning in road networks. After performing some preprocessing steps, we can compute accurate quickest-path lengths in a few microseconds on a 2.0 GHz machine, using real-world road networks with several million nodes. In addition to dealing with the static point-to-point problem, we also handle dynamic scenarios (like traffic jams) and many-to-many instances

Models for energy consumption of data structures and algorithms

EXCESS deliverable D2.1. More information at http://www.excess-project.eu/This deliverable reports our early energy models for data structures and algorithms based on both micro-benchmarks and concurrent algorithms. It reports the early results of Task 2.1 on investigating and modeling the trade-off between energy and performance in concurrent data structures and algorithms, which forms the basis for the whole work package 2 (WP2). The work has been conducted on the two main EXCESS platforms: (1) Intel platform with recent Intel multi-core CPUs and (2) Movidius embedded platform

White-box methodologies, programming abstractions and libraries

EXCESS deliverable D2.2. More information at http://www.excess-project.eu/This deliverable reports the results of white-box methodologies and early results ofthe first prototype of libraries and programming abstractions as available by projectmonth 18 by Work Package 2 (WP2). It reports i) the latest results of Task 2.2on white-box methodologies, programming abstractions and libraries for developingenergy-efficient data structures and algorithms and ii) the improved results of Task2.1 on investigating and modeling the trade-off between energy and performance ofconcurrent data structures and algorithms. The work has been conducted on two mainEXCESS platforms: Intel platforms with recent Intel multicore CPUs and MovidiusMyriad1 platform