@misc{Hoffmann, type = {Master Thesis}, author = {Hoffmann, Marie}, title = {Approximate Algorithms for Distributed Systems}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42370}, school = {Zuse Institute Berlin (ZIB)}, pages = {75}, abstract = {Peer-to-peer (P2P) systems form a special class of distributed systems. Typically, nodes in a P2P system are flat and share the same responsabilities. In this thesis we focus on three problems that occur in P2P systems: the storage of data replicates, quantile computation on distributed data streams, and churn rate estimation. Data replication is one of the oldest techniques to maintain stored data in a P2P system and to reply to read requests. Applications, which use data replication are distributed databases. They are part of an abstract overlay network and do not see the underlying network topology. The question is how to place a set of data replicates in a distributed system such that response times and failure probabilities become minimal without a priori knowledge of the topology of the underlying hardware nodes? We show how to utilize an agglomerative clustering procedure to reach this goal. State-of-the-art algorithms for aggregation of distributed data or data streams require at some point synchronization, or merge data aggregates hierarchically, which does not accompany the basic principle of P2P systems. We test whether randomized communication and merging of data aggregates are able to produce the same results. These data aggregates serve for quantile queries. Constituting and maintaining a P2P overlay network requires frequent message passing. It is a goal to minimize the number of maintenance messages since they consume bandwidth which might be missing for other applications. The lower bound of the frequency for mainte- nance messages is highly dependent on the churn rate of peers. We show how to estimate the mean lifetime of peers and to reduce the frequency for maintenance messages without destabilizing the infrastructure of the constituting overlay.}, language = {en} } @misc{Keidel, type = {Master Thesis}, author = {Keidel, Stefan}, title = {Snapshots in Scalaris}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42282}, school = {Zuse Institute Berlin (ZIB)}, pages = {87}, abstract = {Eines der gr{\"o}ßten Hindernisse beim praktischen Einsatz von Scalaris, einer skalierbaren Implementierung einer verteilten Hashtabelle mit Unterst{\"u}tzung f{\"u}r Transaktionen, ist das Fehlen eines Verfahrens zur Aufnahme eines konsistenten Zustandes des gesamten Systems. Wir stellen in dieser Arbeit ein einfaches Protokoll vor, dass diese Aufgabe erf{\"u}llt und sich, auf Grund der von uns gew{\"a}hlten Herangehensweise, leicht implementieren l{\"a}sst. Als Ausgangspunkt daf{\"u}r w{\"a}hlen wir aus einer Reihe von „klassischen" Snapshot-Algorithmen ein 1993 von Mattern entworfenes Verfahren, welches auf dem Algorithmus von Lai und Yang basiert, aus. Diese Entscheidung basiert auf einer gr{\"u}ndlichen Analyse der Protokolle unter Ber{\"u}cksichtigung der Architektur der existierenden Software. Im n{\"a}chsten Arbeitsschritt benutzen wir unser vollst{\"a}ndiges Wissen {\"u}ber die Interna des Transaktionssystems von Scalaris und vereinfachen damit das Verfahren hinsichtlich Benutzbarkeit und Implementierungskomplexit{\"a}t, ohne die Anforderungen an den aufgenommenen Zustand aufzuweichen. Statt einer losen Anh{\"a}ufung lokaler Zust{\"a}nde der einzelnen Teilnehmerknoten k{\"o}nnen wir am Ende eine große Schl{\"u}ssel-Wert-Tabelle als Ergebnis erzeugen, die konsistent ist, sich leicht weiterverarbeiten l{\"a}sst und die einem Zustand entspricht, in dem sich das System einmal befunden haben k{\"o}nnte. Nachdem wir das Verfahren dann in Software umgesetzt haben, werten wir die Ergebnisse hinsichtlich des Einflusses auf die Performanz des Gesamtsystems aus und diskutieren m{\"o}gliche Weiterentwicklungen.}, language = {de} } @misc{Schaefer, author = {Sch{\"a}fer, Patrick}, title = {Bag-Of-SFA-Symbols in Vector Space (BOSS VS)}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-54984}, abstract = {Time series classification mimics the human understanding of similarity. When it comes to larger datasets, state of the art classifiers reach their limits in terms of unreasonable training or testing times. One representative example is the 1-nearest-neighbor DTW classifier (1-NN DTW) that is commonly used as the benchmark to compare to and has several shortcomings: it has a quadratic time and it degenerates in the presence of noise. To reduce the computational complexity lower bounding techniques or recently a nearest centroid classifier have been introduced. Still, execution times to classify moderately sized datasets on a single core are in the order of hours. We present our Bag-Of-SFA-Symbols in Vector Space (BOSS VS) classifier that is robust and accurate due to invariance to noise, phase shifts, offsets, amplitudes and occlusions. We show that it is as accurate while being multiple orders of magnitude faster than state of the art classifiers. Using the BOSS VS allows for mining massive time series datasets and real-time analytics.}, language = {en} } @misc{WendeSteinke, author = {Wende, Florian and Steinke, Thomas}, title = {Swendsen-Wang Multi-Cluster Algorithm for the 2D/3D Ising Model on Xeon Phi and GPU}, issn = {1438-0064}, doi = {10.1145/2503210.2503254}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42187}, abstract = {Simulations of the critical Ising model by means of local update algorithms suffer from critical slowing down. One way to partially compensate for the influence of this phenomenon on the runtime of simulations is using increasingly faster and parallel computer hardware. Another approach is using algorithms that do not suffer from critical slowing down, such as cluster algorithms. This paper reports on the Swendsen-Wang multi-cluster algorithm on Intel Xeon Phi coprocessor 5110P, Nvidia Tesla M2090 GPU, and x86 multi-core CPU. We present shared memory versions of the said algorithm for the simulation of the two- and three-dimensional Ising model. We use a combination of local cluster search and global label reduction by means of atomic hardware primitives. Further, we describe an MPI version of the algorithm on Xeon Phi and CPU, respectively. Significant performance improvements over known im plementations of the Swendsen-Wang algorithm are demonstrated.}, language = {en} }