TY - GEN A1 - Dreßler, Sebastian A1 - Steinke, Thomas T1 - Automated Analysis of Complex Data Objects T2 - 28th International Supercomputing Conference, ISC 2013, Leipzig, Germany, June 16-20, 2013 Y1 - 2013 N1 - Posterbeitrag ER - TY - JOUR A1 - Grunzke, Richard A1 - Breuers, Sebastian A1 - Gesing, Sandra A1 - Herres-Pawlis, Sonja A1 - Kruse, Martin A1 - Blunk, Dirk A1 - de la Garza, Luis A1 - Packschies, Lars A1 - Schäfer, Patrick A1 - Schärfe, Charlotta A1 - Schlemmer, Tobias A1 - Steinke, Thomas A1 - Schuller, Bernd A1 - Müller-Pfefferkorn, Ralph A1 - Jäkel, René A1 - Nagel, Wolfgang A1 - Atkinson, Malcolm A1 - Krüger, Jens T1 - Standards-based metadata management for molecular simulations JF - Concurrency and Computation: Practice and Experience Y1 - 2013 U6 - https://doi.org/10.1002/cpe.3116 ER - TY - CHAP A1 - Wende, Florian A1 - Steinke, Thomas T1 - Swendsen-Wang Multi-Cluster Algorithm for the 2D/3D Ising Model on Xeon Phi and GPU T2 - 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 Y1 - 2013 U6 - https://doi.org/http://dx.doi.org/10.1145/2503210.2503254 ER - TY - GEN A1 - Dreßler, Sebastian A1 - Steinke, Thomas T1 - An Automated Approach for Estimating the Memory Footprint of Non-Linear Data Objects N2 - Current programming models for heterogeneous devices with disjoint physical memory spaces require explicit allocation of device memory and explicit data transfers. While it is quite easy to manually implement these operations for linear data objects like arrays, this task becomes more difficult for non-linear objects, e.g. linked lists or multiple inherited classes. The difficulties arise due to dynamic memory requirements at run-time and the dependencies between data structures. In this paper we present a novel method to build a graph-based static data type description which is used to create code for injectable functions that automatically determine the memory footprint of data objects at run-time. Our approach is extensible to implement automatically generated optimized data transfers across physical memory spaces. T3 - ZIB-Report - 13-46 KW - memory footprint KW - non-linear objects KW - static analysis KW - dynamic analysis Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-42224 SN - 1438-0064 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 -