@inproceedings{WendeSteinke, author = {Wende, Florian and Steinke, Thomas}, title = {Swendsen-Wang Multi-Cluster Algorithm for the 2D/3D Ising Model on Xeon Phi and GPU}, series = {Proceeding SC '13 Proceedings of SC13: International Conference for High Performance Computing, Networking, Storage and Analysis Article No. 83 ACM New York, NY, USA, 2013}, booktitle = {Proceeding SC '13 Proceedings of SC13: International Conference for High Performance Computing, Networking, Storage and Analysis Article No. 83 ACM New York, NY, USA, 2013}, doi = {http://dx.doi.org/10.1145/2503210.2503254}, language = {en} } @masterthesis{Wende, type = {Bachelor Thesis}, author = {Wende, Florian}, title = {Dynamic Load Balancing on Massively Parallel Computer Architectures}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42166}, school = {Zuse Institute Berlin (ZIB)}, pages = {100}, abstract = {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.}, 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} }