TY - THES A1 - Wende, Florian T1 - Dynamic Load Balancing on Massively Parallel Computer Architectures N2 - This thesis reports on using dynamic load balancing methods on massively parallel computers in the context of multithreaded computations. In particular we investigate the applicability of a randomized work stealing algorithm to ray tracing and breadth-first search as representatives of real-world applications with dynamic work creation. For our considerations we made use of current massively parallel hardware accelerators: Nvidia Tesla M2090, and Intel Xeon Phi. For both of the two we demonstrate the suitability of the work stealing scheme for the said real-world applications. Also the necessity of dynamic load balancing for irregular computations on such hardware is illustrated. N2 - Vorliegende Bachelorarbeit befasst sich mit Methoden der dynamischen Lastbalancierung auf massiv parallelen Computern im Rahmen von mehrprozess gestützten Ausführungen von Programmen. Im einzelnen wird die Eignung eines randomisierten Work-Stealing Algorithmus für die Ausführung realer Anwendungen mit dynamischer Arbeitserzeugung, wie Ray-Tracing und Breitensuche, untersucht. Für die entsprechenden Betrachtungen wer den aktuelle massiv parallele Hardwarebeschleuniger vom Typ Nvidia Tesla M2090 und Intel Xeon Phi verwendet. Für beide Beschleunigertypen konnte die Tauglichkeit des Work-Stealing Schemas für die genannten Anwendungen gezeigt werden. Ebenfalls wird die Notwendigkeit der Verwendung dynamischer Lastausgleichsmethoden für irreguläre Berechnungen auf der genannten Hardware verdeutlicht. Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-42166 ER - TY - GEN A1 - Wende, Florian A1 - Steinke, Thomas T1 - Swendsen-Wang Multi-Cluster Algorithm for the 2D/3D Ising Model on Xeon Phi and GPU N2 - 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. T3 - ZIB-Report - 13-44 KW - Swendsen-Wang Multi-Cluster Algorithm KW - Ising Model KW - Xeon Phi KW - GPGPU KW - Connected Component Labeling Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-42187 SN - 1438-0064 ER -