{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T19:21:48Z","timestamp":1777058508517,"version":"3.51.4"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031695766","type":"print"},{"value":"9783031695773","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-69577-3_22","type":"book-chapter","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T19:02:05Z","timestamp":1724612525000},"page":"313-328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficient Coupling Streaming AI and\u00a0Ensemble Simulations on\u00a0HPC Clusters"],"prefix":"10.1007","author":[{"given":"Jiazhi","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Hongbin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Deyin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jiangsu","family":"Du","sequence":"additional","affiliation":[]},{"given":"Xiaojiao","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Jinhui","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Pin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yutong","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,26]]},"reference":[{"key":"22_CR1","unstructured":"Ior benchmark. https:\/\/openbenchmarking.org\/tests"},{"key":"22_CR2","unstructured":"Plasma. https:\/\/arrow.apache.org\/blog\/2017\/08\/08\/plasma"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Alsaadi, A., et\u00a0al.: Radical-pilot and parsl: executing heterogeneous workflows on hpc platforms. In: 2022 IEEE\/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS), pp. 27\u201334. IEEE (2022)","DOI":"10.1109\/WORKS56498.2022.00009"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Brace, A., et\u00a0al.: Coupling streaming ai and hpc ensembles to achieve 100\u20131000$$\\times $$ faster biomolecular simulations. In: 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 806\u2013816. IEEE (2022)","DOI":"10.1109\/IPDPS53621.2022.00083"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Docan, C., et\u00a0al.: Dataspaces: an interaction and coordination framework for coupled simulation workflows. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, pp. 25\u201336 (2010)","DOI":"10.1145\/1851476.1851481"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Jha, S.e.a.: Ai-coupled hpc workflows. arXiv preprint arXiv:2208.11745 (2022)","DOI":"10.1142\/9789811265679_0028"},{"issue":"1","key":"22_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-022-3657-3","volume":"67","author":"J Jiang","year":"2024","unstructured":"Jiang, J., et al.: Htdcr: a job execution framework for high-throughput computing on supercomputers. SCIENCE CHINA Inf. Sci. 67(1), 112104 (2024)","journal-title":"SCIENCE CHINA Inf. Sci."},{"issue":"7","key":"22_CR8","first-page":"705","volume":"42","author":"M Khaldi","year":"2020","unstructured":"Khaldi, M., et al.: Fault tolerance for a scientific workflow system in a cloud computing environment. Int. J. Comput. Appl. 42(7), 705\u2013714 (2020)","journal-title":"Int. J. Comput. Appl."},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Mathuriya, A., et\u00a0al.: Cosmoflow: using deep learning to learn the universe at scale. In: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 819\u2013829. IEEE (2018)","DOI":"10.1109\/SC.2018.00068"},{"issue":"4","key":"22_CR10","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1109\/TPDS.2021.3105994","volume":"33","author":"A Merzky","year":"2021","unstructured":"Merzky, A., et al.: Design and performance characterization of radical-pilot on leadership-class platforms. IEEE Trans. Parallel Distrib. Syst. 33(4), 818\u2013829 (2021)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"22_CR11","unstructured":"Moritz, P., et\u00a0al.: Ray: a distributed framework for emerging $$\\{$$AI$$\\}$$ applications. In: 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), pp. 561\u2013577 (2018)"},{"key":"22_CR12","unstructured":"Natale, F.: Maestro workflow conductor. In: Lawrence Livermore National Laboratory (2018)"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Pascuzzi, V.R., et\u00a0al.: Asynchronous execution of heterogeneous tasks in ml-driven hpc workflows. In: Workshop on Job Scheduling Strategies for Parallel Processing, pp. 27\u201345. Springer (2023)","DOI":"10.1007\/978-3-031-43943-8_2"},{"key":"22_CR14","unstructured":"Peterson, J.L., et\u00a0al.: Merlin: enabling machine learning-ready hpc ensembles. Technical report, Lawrence Livermore National Lab., Livermore, CA (United States) (2019)"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Saadi, A.A., et\u00a0al.: Impeccable: Integrated modeling pipeline for covid cure by assessing better leads. In: Proceedings of the 50th International Conference on Parallel Processing, pp. 1\u201312 (2021)","DOI":"10.1145\/3472456.3473524"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Subedi, P., et\u00a0al.: Rise: Reducing i\/o contention in staging-based extreme-scale in-situ workflows. In: 2021 IEEE International Conference on Cluster Computing, pp. 146\u2013156. IEEE (2021)","DOI":"10.1109\/Cluster48925.2021.00021"},{"key":"22_CR17","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1007\/s11390-020-9797-6","volume":"35","author":"MA Vef","year":"2020","unstructured":"Vef, M.A., et al.: Gekkofs-a temporary burst buffer file system for hpc applications. J. Comput. Sci. Technol. 35, 72\u201391 (2020)","journal-title":"J. Comput. Sci. Technol."},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Ward, L., et\u00a0al.: Colmena: scalable machine-learning-based steering of ensemble simulations for high performance computing. In: 2021 IEEE\/ACM Workshop on Machine Learning in High Performance Computing Environments. IEEE (2021)","DOI":"10.1109\/MLHPC54614.2021.00007"},{"issue":"10","key":"22_CR19","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1038\/s41592-021-01254-9","volume":"18","author":"L Wratten","year":"2021","unstructured":"Wratten, L., et al.: Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers. Nat. Methods 18(10), 1161\u20131168 (2021)","journal-title":"Nat. Methods"}],"container-title":["Lecture Notes in Computer Science","Euro-Par 2024: Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-69577-3_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T19:07:58Z","timestamp":1724612878000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-69577-3_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031695766","9783031695773"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-69577-3_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"26 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Euro-Par","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"europar2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.euro-par.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}