{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T07:22:35Z","timestamp":1774596155292,"version":"3.50.1"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439421","type":"print"},{"value":"9783031439438","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43943-8_8","type":"book-chapter","created":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T13:52:25Z","timestamp":1694699545000},"page":"155-172","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Evaluating the\u00a0Potential of\u00a0Coscheduling on\u00a0High-Performance Computing Systems"],"prefix":"10.1007","author":[{"given":"Jason","family":"Hall","sequence":"first","affiliation":[]},{"given":"Arjun","family":"Lathi","sequence":"additional","affiliation":[]},{"given":"David K.","family":"Lowenthal","sequence":"additional","affiliation":[]},{"given":"Tapasya","family":"Patki","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,15]]},"reference":[{"key":"8_CR1","unstructured":"Mckernel. https:\/\/www.pccluster.org\/en\/mckernel\/download.html"},{"key":"8_CR2","unstructured":"Slurm workload manager. https:\/\/slurm.schedmd.com\/core_spec.html"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Ahn, D.H., Garlick, J., Grondona, M., Lipari, D., Springmeyer, B., Schulz, M.: Flux: A next-generation resource management framework for large HPC centers. In: Workshop on Scheduling and Resource Management for Parallel and Distributed Systems (September 2014)","DOI":"10.1109\/ICPPW.2014.15"},{"key":"8_CR4","unstructured":"AMD: AMD ROCm Platform Reference. https:\/\/cgmb-rocm-docs.readthedocs.io\/en\/latest\/index.html"},{"issue":"3","key":"8_CR5","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1145\/380749.380764","volume":"19","author":"AC Arpaci-Dusseau","year":"2001","unstructured":"Arpaci-Dusseau, A.C.: Implicit coscheduling: coordinated scheduling with implicit information in distributed systems. ACM Trans. Comput. Syst. 19(3), 283\u2013331 (2001)","journal-title":"ACM Trans. Comput. Syst."},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Batat, A., Feitelson, D.: Gang scheduling with memory considerations. In: International Parallel and Distributed Processing Symposium, pp. 109\u2013114 (2000)","DOI":"10.1109\/IPDPS.2000.845971"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Beckman, P., Iskra, K., Yoshii, K., Coghlan, S.: The influence of operating systems on the performance of collective operations at extreme scale. In: International Conference on Cluster Computing (Sep 2006)","DOI":"10.1109\/CLUSTR.2006.311846"},{"issue":"4","key":"8_CR8","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1145\/2499368.2451125","volume":"48","author":"C Delimitrou","year":"2013","unstructured":"Delimitrou, C., Kozyrakis, C.: Paragon: QoS-aware scheduling for heterogeneous datacenters. SIGPLAN Not. 48(4), 77\u201388 (2013)","journal-title":"SIGPLAN Not."},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Fan, Y., Lan, Z., Rich, P., Allcock, W.E., Papka, M.E.: Hybrid workload scheduling on HPC systems. In: 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (2022)","DOI":"10.1109\/IPDPS53621.2022.00052"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Feitelson, D.G., Rudolph, L.: Parallel job scheduling: issues and approaches. In: Workshop on Job Scheduling Strategies for Parallel Processing (1995)","DOI":"10.1007\/3-540-60153-8"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Ferreira, K.B., Bridges, P., Brightwell, R.: Characterizing application sensitivity to OS interference using kernel-level noise injection. In: SC 2008: Proceedings of the 2008 ACM\/IEEE Conference on Supercomputing (Supercomputing) (Nov 2008)","DOI":"10.1109\/SC.2008.5219920"},{"key":"8_CR12","unstructured":"Flux Framework Team: 2014 Cab supercomputer job scheduling traces (2020)"},{"key":"8_CR13","unstructured":"Franke, H., Pattnaik, P., Rudolph, L.: Gang scheduling for highly efficient, distributed multiprocessor systems. In: Proceedings of the 6th Symposium on the Frontiers of Massively Parallel Computation (1996)"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Garg, R., De, P.: Impact of noise on scaling of collectives: an empirical evaluation. In: International Conference on High Performance Computing (Dec 2006)","DOI":"10.1007\/11945918_45"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Govindan, S., Liu, J., Kansal, A., Sivasubramaniam, A.: Cuanta: quantifying effects of shared on-chip resource interference for consolidated virtual machines. In: ACM Symposium on Cloud Computing (2011)","DOI":"10.1145\/2038916.2038938"},{"key":"8_CR16","unstructured":"Hindman, B., et al.: Mesos: a platform for fine-grained resource sharing in the data center. In: Networked Systems Design and Implementation (2011)"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Hoefler, T., Schneider, T., Lumsdaine, A.: Characterizing the influence of system noise on large-scale applications by simulation. In: Proceedings of the 2010 ACM\/IEEE Conference on Supercomputing (Supercomputing) (Nov 2010)","DOI":"10.1109\/SC.2010.12"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Jackson, D., Snell, Q., Clement, M.: Core Algorithms of the Maui Scheduler. In: Job Scheduling Strategies for Parallel Processing (2001)","DOI":"10.1007\/3-540-45540-X_6"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Lange, J., et al.: Palacios and Kitten: new high performance operating systems for scalable virtualized and native supercomputing. In: Proceedings of the 24th IEEE International Parallel and Distributed Processing Symposium (April 2010)","DOI":"10.1109\/IPDPS.2010.5470482"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Li, B., Patel, T., Samsi, S., Gadepally, V., Tiwari, D.: MISO: Exploiting multi-instance GPU capability on multi-tenant GPU clusters. In: Proceedings of the 13th Symposium on Cloud Computing (2022)","DOI":"10.1145\/3542929.3563510"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Lifka, D.: The ANL\/IBM SP Scheduling System. In: Job Scheduling Strategies for Parallel Processing. vol. 949, pp. 295\u2013303 (1995)","DOI":"10.1007\/3-540-60153-8_35"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Mars, J., Tang, L., Hundt, R., Skadron, K., Soffa, M.L.: Bubble-up: increasing utilization in modern warehouse scale computers via sensible co-locations. In: IEEE\/ACM International Symposium on Microarchitecture (2011)","DOI":"10.1145\/2155620.2155650"},{"key":"8_CR23","unstructured":"Meuer, H., Strohmaier, E., Dongarra, J., Simon, H.: Top500 Supercomputer Sites (2022). http:\/\/www.top500.org"},{"issue":"6","key":"8_CR24","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1109\/71.932708","volume":"12","author":"AW Mu\u2019alem","year":"2001","unstructured":"Mu\u2019alem, A.W., Feitelson, D.: Utilization, predictability, workloads, and user runtime estimates in scheduling the IBM SP2 with backfilling. IEEE Trans. Parallel Distrib. Syst. 12(6), 529\u2013543 (2001)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Nathuji, R., Kansal, A., Ghaffarkhah, A.: Q-clouds: managing performance interference effects for QoS-aware clouds. In: Proceedings of the 5th European Conference on Computer Systems (EuroSys) (2010)","DOI":"10.1145\/1755913.1755938"},{"key":"8_CR26","unstructured":"NVIDIA Corp.: NVML API Reference Guide. https:\/\/docs.nvidia.com\/deploy\/nvml-api\/index.html"},{"key":"8_CR27","unstructured":"Palantir, I.: Spark scheduling in kubernetes (May 2019). https:\/\/medium.com\/palantir\/spark-scheduling-in-kubernetes-4976333235f3"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Patki, T., et al.: Practical resource management in power-constrained. high-performance distributed computing In: High Performance Computing (Jun 2015)","DOI":"10.1145\/2749246.2749262"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Petrini, F., Kerbyson, D.J., Pakin, S.: The case of the missing supercomputer performance: Achieving optimal performance on the 8,192 processors of ASCI Q. In: SC 2003: Proceedings of the 2003 ACM\/IEEE Conference on Supercomputing (Supercomputing) (2003)","DOI":"10.1145\/1048935.1050204"},{"key":"8_CR30","unstructured":"Pottier, L.: Co-scheduling for large-scale applications : memory and resilience. Ph.D. thesis, Universit\u00e9 de Lyon (2018) https:\/\/theses.hal.science\/tel-01892395\/"},{"key":"8_CR31","unstructured":"Rensin, D.K.: Kubernetes - Scheduling the Future at Cloud Scale. 1005 Gravenstein Highway North Sebastopol, CA 95472 (2015). http:\/\/www.oreilly.com\/webops-perf\/free\/kubernetes.csp"},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Saba, I., Arima, E., Liu, D., Schulz, M.: Orchestrated co-scheduling, resource partitioning, and power capping on CPU-GPU heterogeneous systems via machine learning. In: Architecture of Computing Systems (2022)","DOI":"10.1007\/978-3-031-21867-5_4"},{"key":"8_CR33","unstructured":"Saha, P., Beltre, A., Govindaraju, M.: Scylla: A Mesos framework for container based MPI jobs. CoRR abs\/1905.08386 (2019). http:\/\/arxiv.org\/abs\/1905.08386"},{"key":"8_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1007\/10968987_12","volume-title":"Job Scheduling Strategies for Parallel Processing","author":"E Shmueli","year":"2003","unstructured":"Shmueli, E., Feitelson, D.G.: Backfilling with lookahead to optimize the performance of parallel job scheduling. In: Feitelson, D., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2003. LNCS, vol. 2862, pp. 228\u2013251. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/10968987_12"},{"key":"8_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/BFb0022286","volume-title":"Job Scheduling Strategies for Parallel Processing","author":"J Skovira","year":"1996","unstructured":"Skovira, J., Chan, W., Zhou, H., Lifka, D.: The EASY \u2014 LoadLeveler API project. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 1996. LNCS, vol. 1162, pp. 41\u201347. Springer, Heidelberg (1996). https:\/\/doi.org\/10.1007\/BFb0022286"},{"key":"8_CR36","doi-asserted-by":"crossref","unstructured":"Smith, S.A., et al.: Mitigating inter-job interference using adaptive flow-aware routing. In: Supercomputing (Nov 2018)","DOI":"10.1109\/SC.2018.00030"},{"key":"8_CR37","unstructured":"Rennich, S.: CUDA C\/C++ Streams and Concurrency. https:\/\/developer.download.nvidia.com\/CUDA\/training\/StreamsAndConcurrencyWebinar.pdf"},{"key":"8_CR38","doi-asserted-by":"crossref","unstructured":"Tang, X., et al.: Spread-n-share: Improving application performance and cluster throughput with resource-aware job placement. In: Supercomputing (2019)","DOI":"10.1145\/3295500.3356152"},{"key":"8_CR39","unstructured":"The BlueGene\/L Team: An overview of the BlueGene\/L supercomputer. In: Proceedings of the 2002 ACM\/IEEE Conference on Supercomputing (SC 2002) (2002)"},{"key":"8_CR40","doi-asserted-by":"crossref","unstructured":"Tsafrir, D., Etsion, Y., Feitelson, D., Kirkpatrick, S.: System noise, OS clock ticks, and fine-grained parallel applications. In: International Conference on Supercomputing (Jun 2005)","DOI":"10.1145\/1088149.1088190"},{"key":"8_CR41","doi-asserted-by":"crossref","unstructured":"Vavilapalli, V.K., et al.: Apache hadoop YARN: yet another resource negotiator. In: Proceedings of the 4th Annual Symposium on Cloud Computing (2013)","DOI":"10.1145\/2523616.2523633"},{"key":"8_CR42","doi-asserted-by":"crossref","unstructured":"Vazhkudai, S.S., et al.: The Design, Deployment, and Evaluation of the CORAL Pre-Exascale Systems. In: Supercomputing (November 2018)","DOI":"10.1109\/SC.2018.00055"},{"key":"8_CR43","doi-asserted-by":"crossref","unstructured":"Verma, A., Ahuja, P., Neogi, A.: Power-aware dynamic placement of HPC applications. In: International Conference on Supercomputing (2008)","DOI":"10.1145\/1375527.1375555"},{"issue":"6","key":"8_CR44","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1109\/TPDS.2003.1206505","volume":"14","author":"Y Wiseman","year":"2003","unstructured":"Wiseman, Y., Feitelson, D.: Paired gang scheduling. IEEE Trans. Parallel Distrib. Syst. 14(6), 581\u2013592 (2003)","journal-title":"IEEE Trans. Parallel Distrib. Syst."}],"container-title":["Lecture Notes in Computer Science","Job Scheduling Strategies for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43943-8_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T02:11:56Z","timestamp":1730081516000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43943-8_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439421","9783031439438"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43943-8_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"JSSPP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Job Scheduling Strategies for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"St. Petersburg, FL","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"jsspp2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"9","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"64% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.9","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}