@misc{DoebbelinSchuettReinefeld, author = {D{\"o}bbelin, Robert and Sch{\"u}tt, Thorsten and Reinefeld, Alexander}, title = {Building Large Compressed PDBs for the Sliding Tile Puzzle}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-18095}, abstract = {The performance of heuristic search algorithms depends crucially on the effectiveness of the heuristic. A pattern database (PDB) is a powerful heuristic in the form of a pre-computed lookup table. Larger PDBs provide better bounds and thus allow more cut-offs in the search process. Today, the largest PDB for the 24-puzzle is a 6-6-6-6 PDB with a size of 486 MB. We created 8-8-8, 9-8-7 and 9-9-6 PDBs that are three orders of magnitude larger (up to 1.4 TB) than the 6-6-6-6 PDB. We show how to compute such large PDBs and we present statistical and empirical data on their efficiency. The largest single PDB gives on average an 8-fold improvement over the 6-6-6-6 PDB. Combining several large PDBs gives on average an 12-fold improvement.}, language = {en} } @misc{Pyszkalski, author = {Pyszkalski, Wolfgang}, title = {{\"U}bersicht {\"u}ber die Datenhaltung im ZIB und die M{\"o}glichkeiten einer Nutzung durch Projekte}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42060}, abstract = {Dieser Bericht beschreibt in allgemein verst{\"a}ndlicher Form die Struktur der Datenhaltung im Zuse-Institut Berlin (ZIB) und die aktuell verwendeten Strategien im Umgang mit immer st{\"a}rker wachsenden Datenmengen, sowie der Herausforderung der Speicherung unwiederbringlicher Daten {\"u}ber im Grunde unbegrenzte Zeit.}, language = {de} } @misc{DresslerSteinke, author = {Dreßler, Sebastian and Steinke, Thomas}, title = {An Automated Approach for Estimating the Memory Footprint of Non-Linear Data Objects}, issn = {1438-0064}, doi = {10.1007/978-3-642-54420-0_25}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42224}, abstract = {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.}, 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} }