{"series_title":"Lecture Notes in Computer Science","date_updated":"2022-01-06T06:55:22Z","type":"conference","conference":{"name":"21st Workshop on Job Scheduling Strategies for Parallel Processing","location":"Orlando, FL, USA","end_date":"2017-06-02","start_date":"2017-06-02"},"publication":"Proc. Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP)","language":[{"iso":"eng"}],"title":"A Data Structure for Planning Based Workload Management of Heterogeneous HPC Systems","_id":"22","department":[{"_id":"27"}],"publication_identifier":{"isbn":["978-3-319-77398-8","978-3-319-77397-1"]},"volume":10773,"user_id":"15274","citation":{"short":"A. Keller, in: D. Klusáček, W. Cirne, N. Desai (Eds.), Proc. Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP), Springer, 2018, pp. 132–151.","chicago":"Keller, Axel. “A Data Structure for Planning Based Workload Management of Heterogeneous HPC Systems.” In <i>Proc. Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP)</i>, edited by D. Klusáček, W. Cirne, and N. Desai, 10773:132–51. Lecture Notes in Computer Science. Springer, 2018. <a href=\"https://doi.org/10.1007/978-3-319-77398-8_8\">https://doi.org/10.1007/978-3-319-77398-8_8</a>.","bibtex":"@inproceedings{Keller_2018, series={Lecture Notes in Computer Science}, title={A Data Structure for Planning Based Workload Management of Heterogeneous HPC Systems}, volume={10773}, DOI={<a href=\"https://doi.org/10.1007/978-3-319-77398-8_8\">10.1007/978-3-319-77398-8_8</a>}, booktitle={Proc. Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP)}, publisher={Springer}, author={Keller, Axel}, editor={Klusáček, D. and Cirne, W. and Desai, N.Editors}, year={2018}, pages={132–151}, collection={Lecture Notes in Computer Science} }","apa":"Keller, A. (2018). A Data Structure for Planning Based Workload Management of Heterogeneous HPC Systems. In D. Klusáček, W. Cirne, &#38; N. Desai (Eds.), <i>Proc. Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP)</i> (Vol. 10773, pp. 132–151). Orlando, FL, USA: Springer. <a href=\"https://doi.org/10.1007/978-3-319-77398-8_8\">https://doi.org/10.1007/978-3-319-77398-8_8</a>","ieee":"A. Keller, “A Data Structure for Planning Based Workload Management of Heterogeneous HPC Systems,” in <i>Proc. Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP)</i>, Orlando, FL, USA, 2018, vol. 10773, pp. 132–151.","ama":"Keller A. A Data Structure for Planning Based Workload Management of Heterogeneous HPC Systems. In: Klusáček D, Cirne W, Desai N, eds. <i>Proc. Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP)</i>. Vol 10773. Lecture Notes in Computer Science. Springer; 2018:132-151. doi:<a href=\"https://doi.org/10.1007/978-3-319-77398-8_8\">10.1007/978-3-319-77398-8_8</a>","mla":"Keller, Axel. “A Data Structure for Planning Based Workload Management of Heterogeneous HPC Systems.” <i>Proc. Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP)</i>, edited by D. Klusáček et al., vol. 10773, Springer, 2018, pp. 132–51, doi:<a href=\"https://doi.org/10.1007/978-3-319-77398-8_8\">10.1007/978-3-319-77398-8_8</a>."},"status":"public","intvolume":"     10773","year":"2018","author":[{"first_name":"Axel","id":"15274","last_name":"Keller","full_name":"Keller, Axel"}],"publisher":"Springer","doi":"10.1007/978-3-319-77398-8_8","page":"132-151","publication_status":"published","keyword":["Scheduling Planning Mapping Workload management"],"editor":[{"full_name":"Klusáček, D.","last_name":"Klusáček","first_name":"D."},{"full_name":"Cirne, W.","last_name":"Cirne","first_name":"W."},{"first_name":"N.","full_name":"Desai, N.","last_name":"Desai"}],"date_created":"2017-07-25T14:54:08Z","abstract":[{"text":"This paper describes a data structure and a heuristic to plan and map arbitrary resources in complex combinations while applying time dependent constraints. The approach is used in the planning based workload manager OpenCCS at the Paderborn Center for Parallel Computing (PC\\(^2\\)) to operate heterogeneous clusters with up to 10000 cores. We also show performance results derived from four years of operation.","lang":"eng"}]}