@inproceedings{WeinholdLackorzynskiBierbaumetal.2019, author = {Weinhold, Carsten and Lackorzynski, Adam and Bierbaum, Jan and K{\"u}ttler, Martin and Planeta, Maksym and Weisbach, Hannes and Hille, Matthias and H{\"a}rtig, Hermann and Margolin, Alexander and Sharf, Dror and Levy, Ely and Gak, Pavel and Barak, Amnon and Gholami, Masoud and Schintke, Florian and Sch{\"u}tt, Thorsten and Reinefeld, Alexander and Lieber, Matthias and Nagel, Wolfgang}, title = {FFMK: A Fast and Fault-Tolerant Microkernel-Based System for Exascale Computing}, booktitle = {Software for Exascale Computing - SPPEXA 2016-2019}, publisher = {Springer}, doi = {10.1007/978-3-030-47956-5_16}, pages = {483 -- 516}, year = {2019}, language = {en} } @misc{DoebbelinSchuettReinefeld2013, 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}, year = {2013}, 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} } @article{KruberHoegqvistSchuett2011, author = {Kruber, Nico and H{\"o}gqvist, Mikael and Sch{\"u}tt, Thorsten}, title = {The Benefits of Estimated Global Information in DHT Load Balancing}, volume = {0}, journal = {Cluster Computing and the Grid, IEEE International Symposium on}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, doi = {10.1109/CCGrid.2011.11}, pages = {382 -- 391}, year = {2011}, language = {en} } @inproceedings{DoebbelinSchuettReinefeld2012, author = {D{\"o}bbelin, Robert and Sch{\"u}tt, Thorsten and Reinefeld, Alexander}, title = {An Analysis of SMP Memory Allocators}, booktitle = {Proceedings of the 41st International Conference on Parallel Processing Workshops (Fifth International Workshop on Parallel Programming Models and Systems Software for High-End Computing (P2S2))}, publisher = {IEEE Computer Society}, doi = {10.1109/ICPPW.2012.10}, pages = {48 -- 54}, year = {2012}, language = {en} } @inproceedings{SchuettDoebbelinReinefeld2013, author = {Sch{\"u}tt, Thorsten and D{\"o}bbelin, Robert and Reinefeld, Alexander}, title = {Forward Perimeter Search with Controlled Use of Memory}, booktitle = {International Joint Conference on Artificial Intelligence, IJCAI-13, Beijing}, year = {2013}, language = {en} } @article{SchuettReinefeldDoebbelin2011, author = {Sch{\"u}tt, Thorsten and Reinefeld, Alexander and D{\"o}bbelin, Robert}, title = {MR-search: massively parallel heuristic search}, volume = {25}, journal = {Concurrency and Computation: Practice and Experience}, number = {1}, doi = {10.1002/cpe.1833}, pages = {40 -- 54}, year = {2011}, language = {en} } @article{SalemSchintkeSchuettetal.2019, author = {Salem, Farouk and Schintke, Florian and Sch{\"u}tt, Thorsten and Reinefeld, Alexander}, title = {Improving the throughput of a scalable FLESnet using the Data-Flow Scheduler}, journal = {CBM Progress Report 2018}, isbn = {978-3-9815227-6-1}, doi = {10.15120/GSI-2019-01018}, pages = {149 -- 150}, year = {2019}, language = {en} } @inproceedings{HartungSchintkeSchuett2019, author = {Hartung, Marc and Schintke, Florian and Sch{\"u}tt, Thorsten}, title = {Pinpoint Data Races via Testing and Classification}, booktitle = {2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW); 3rd International Workshop on Software Faults (IWSF 2019)}, doi = {10.1109/ISSREW.2019.00100}, pages = {386 -- 393}, year = {2019}, language = {en} } @article{SalemSchintkeSchuettetal.2020, author = {Salem, Farouk and Schintke, Florian and Sch{\"u}tt, Thorsten and Reinefeld, Alexander}, title = {Scheduling data streams for low latency and high throughput on a Cray XC40 using Libfabric}, volume = {32}, journal = {Concurrency and Computation Practice and Experience}, number = {20}, doi = {10.1002/cpe.5563}, pages = {1 -- 14}, year = {2020}, language = {en} } @article{SkrzypczakSchintkeSchuett2020, author = {Skrzypczak, Jan and Schintke, Florian and Sch{\"u}tt, Thorsten}, title = {RMWPaxos: Fault-Tolerant In-Place Consensus Sequences}, volume = {31}, journal = {IEEE Transactions on Parallel and Distributed Systems}, number = {10}, issn = {1045-9219}, arxiv = {http://arxiv.org/abs/2001.03362}, doi = {10.1109/TPDS.2020.2981891}, pages = {2392 -- 2405}, year = {2020}, language = {en} }