Practical Computer Science
Projects
Practical Computer Science 


A Generic Scheduling Component for Multi-processor Task Programming

Logo genMTS

Overview

A current challenge in high performance computing lies in the achievement of a high scalability of parallel applications for high numbers of processors. The scalability of many parallel algorithms can be significantly improved by adopting a programming model based on separate modules (multi-processor tasks or M-tasks).
The basis for an efficient execution of the emerging M-task graph is an appropriate schedule that defines a suitable mapping of M-tasks to processor groups based on the structure of the considered application and the computational and communication performance of the underlying platform.
The goal of the research project is the development of a generic scheduling component that makes different scheduling algorithms for this scheduling problem available in form of a toolkit. The toolkit should be able to cooperate with other programming tools from the area of M-task programming.

The project is funded by the German Research Foundation (DFG).
Grant Numbers: RU591/9-1 and RU591/9-2.

Publications

  • Dümmler, J.; Schulze, M.: Extending the Scheduling Toolkit SEParAT to Support Hybrid Parallel Platforms. In: International Journal of Computer Theory and Engineering, vol. 8, no. 4: pp. 265-271. IACSIT Press  –  ISSN 1793-8201, 2016. DOI: 10.7763/IJCTE.2016.V8.1056 Online resource available
  • Dümmler, J.; Egerland, S.: Interval-based Performance Modeling for the All-Pairs-Shortest-Paths-Problem on GPUs. In: The Journal of Supercomputing, vol. 71, no. 11: pp. 4192-4214. Springer  –  ISSN 0920-8542, 2015. DOI: 10.1007/s11227-015-1514-9 Online resource available
  • Lang, J.; Rünger, G.: An execution time and energy model for an energy-aware execution of a conjugate gradient method with CPU/GPU collaboration. In: Journal of Parallel and Distributed Computing, vol. 74, no. 9: pp. 2884-2897. Elsevier  –  ISSN 0743-7315, 2014. DOI: 10.1016/j.jpdc.2014.06.001 Online resource available
  • Kunis, R.: Realisierung einer Schedulingumgebung für gemischt-parallele Anwendungen und Optimierung von layer-basierten Schedulingalgorithmen, TU Chemnitz, Fakultät für Informatik, Doctoral thesis, 2011. Online resource available
  • Kunis, R.; Rünger, G.: Optimizing layer-based scheduling algorithms for parallel tasks with dependencies. In: Concurrency and Computation: Practice and Experience, vol. 23, no. 8: pp. 827-849. John Wiley & Sons, Ltd.  –  ISSN 1532-0634, 2011. DOI: 10.1002/cpe.1674 Online resource available
  • Kunis, R.; Rünger, G.: Task-Block Identification and Movement for Layer-based Scheduling Algorithms. In: Smari, W. (Eds.): Proc. of the 2010 International Conference on High Performance Computing & Simulation (HPCS 2010): pp. 132-139. IEEE  –  ISBN 978-1-4244-6828-7. Caen, France, 2010.
  • Kunis, R.; Rünger, G.: Optimization of Layer-based Scheduling Algorithms for Mixed Parallel Applications with Precedence Constraints Using Move-blocks. In: Proc. of the 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2009): pp. 70-77. IEEE Computer Society  –  ISBN 978-0-7695-3544-9. Weimar, Germany, 2009.
  • Dümmler, J.; Kunis, R.; Rünger, G.: Layer-Based Scheduling Algorithms for Multiprocessor-Tasks with Precedence Constraints. In: Parallel Computing: Architectures, Algorithms and Applications: Proc. of the International Conference ParCo 2007 (Advances in Parallel Computing, vol. 15): pp. 321-328. IOS Press  –  ISBN 978-1-58603-796-3. Jülich/Aachen, Germany, 2007. Online resource available
  • Dümmler, J.; Kunis, R.; Rünger, G.: A Scheduling Toolkit for Multiprocessor-Task Programming with Dependencies. In: Proc. of the 13th International Euro-Par Conference (LNCS, vol. 4641): pp. 23-32. Springer  –  ISBN 978-3-540-74465-8. Rennes, France, August 2007. Online resource available
  • Dümmler, J.; Kunis, R.; Rünger, G.: A Comparison of Scheduling Algorithms for Multiprocessortasks with Precedence Constraints. In: Proc. of the 2007 High Performance Computing & Simulation (HPCS'07) Conference: pp. 663-669. ECMS  –  ISBN 978-0-9553018-27. Prague, Czech Republic, 2007.