{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T08:27:53Z","timestamp":1759134473506,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":59,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"U.S. Department of Energy Exascale Computing Project","award":["17-SC-20-SC"],"award-info":[{"award-number":["17-SC-20-SC"]}]},{"name":"Southern Illinois University Carbondale"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,14]]},"DOI":"10.1145\/3458817.3476197","type":"proceedings-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T05:10:34Z","timestamp":1634793034000},"page":"1-15","source":"Crossref","is-referenced-by-count":7,"title":["Bootstrapping in-situ workflow auto-tuning via combining performance models of component applications"],"prefix":"10.1145","author":[{"given":"Tong","family":"Shu","sequence":"first","affiliation":[{"name":"Southern Illinois University"}]},{"given":"Yanfei","family":"Guo","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory"}]},{"given":"Justin","family":"Wozniak","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory"}]},{"given":"Xiaoning","family":"Ding","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology"}]},{"given":"Ian","family":"Foster","sequence":"additional","affiliation":[{"name":"Univ. Chicago"}]},{"given":"Tahsin","family":"Kurc","sequence":"additional","affiliation":[{"name":"Stony Brook University"}]}],"member":"320","published-online":{"date-parts":[[2021,11,13]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"ADIOS.2021. https:\/\/csmd.ornl.gov\/adios. ADIOS.2021. https:\/\/csmd.ornl.gov\/adios."},{"volume-title":"Compiler Techniques for Massively Scalable Implicit Task Parallelism. In IEEE\/ACM Intl. Conf. on High Performance Computing, Networking, Storage and Analysis (SC). 299--310","author":"Armstrong Timothy G.","key":"e_1_3_2_2_2_1"},{"volume-title":"IEEE\/ACM Intl. Conf. on High Performance Computing, Networking, Storage and Analysis (SC).","year":"2016","author":"Ayachit Utkarsh","key":"e_1_3_2_2_3_1"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1109\/JPROC.2018.2841200","article-title":"Autotuning in High-performance Computing Applications","volume":"106","author":"Balaprakash Prasanna","year":"2018","journal-title":"Proc. IEEE"},{"volume-title":"Wild","year":"2013","author":"Balaprakash Prasanna","key":"e_1_3_2_2_5_1"},{"key":"e_1_3_2_2_6_1","article-title":"Optimizing I\/O Performance of HPC Applications with Autotuning","volume":"5","author":"Behzad Babak","year":"2019","journal-title":"ACM Trans. on Parallel Computing (TOPC)"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Alexandra Calotoiu Marcin Copik Torsten Hoefler Marcus Ritter Sergei Shudler and Felix Wolf. 2020. ExtraPeak: Advanced Automatic Performance Modeling for HPC Applications. In Spring Software for Exascale Computing. 453--482. Alexandra Calotoiu Marcin Copik Torsten Hoefler Marcus Ritter Sergei Shudler and Felix Wolf. 2020. ExtraPeak: Advanced Automatic Performance Modeling for HPC Applications. In Spring Software for Exascale Computing. 453--482.","DOI":"10.1007\/978-3-030-47956-5_15"},{"volume-title":"IEEE\/ACM Intl. Conf. on High Performance Computing, Networking, Storage and Analysis (SC). 1--12","year":"2013","author":"Calotoiu Alexandra","key":"e_1_3_2_2_8_1"},{"volume-title":"USENIX Annual Technical Conference (ATC). 893--907","year":"2018","author":"Cao Zhen","key":"e_1_3_2_2_9_1"},{"volume-title":"XGBoost: A Scalable Tree Boosting System. In ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (KDD). 785--794","year":"2016","author":"Chen Tianqi","key":"e_1_3_2_2_10_1"},{"volume-title":"End-to-end Performance Modeling of Distributed GPU Applications. In ACM International Conference on Supercomputing (ICS). 30:1--12","year":"2020","author":"Choi Jaemin","key":"e_1_3_2_2_11_1"},{"volume-title":"Symp. on Cluster, Cloud, and Internet Computing (CCGrid). 246--255","year":"2014","author":"Dayal Jai","key":"e_1_3_2_2_12_1"},{"volume-title":"ACM International Conference on Performance Engineering (ICPE). 145--156","year":"2015","author":"Didona Diego","key":"e_1_3_2_2_13_1"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s10586-011-0162-y","article-title":"DataSpaces: An Interaction and Coordination Framework for Coupled Simulation Workflows","volume":"15","author":"Docan Ciprian","year":"2012","journal-title":"Cluster Computing"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/abcf88"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","DOI":"10.2172\/1372113","volume-title":"Decaf: Decoupled dataflows for in situ high-performance workflows. Technical Report ANL\/MCS-TM-371. ANL.","author":"Dreher Matthieu","year":"2017"},{"key":"e_1_3_2_2_17_1","first-page":"1","article-title":"Addressing Data Resiliency for Staging Based Scientific Workflows. In IEEE\/ACM Intl. Conf. on High Performance Computing","volume":"87","author":"Duan Shaohua","year":"2019","journal-title":"Networking, Storage and Analysis (SC)."},{"volume-title":"Active learning in performance analysis","author":"Duplyakin Dmitry","key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","DOI":"10.1109\/CLUSTER.2016.63"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"Ian Foster Mark Ainsworth Julie Bessac Franck Cappello Jong Choi Sheng Di Zichao Di Ali M Gok Hanqi Guo Kevin A Huck Christopher Kelly Scott Klasky Kerstin Kleese van Dam Xin Liang Kshitij Mehta Manish Parashar Tom Peterka Line Pouchard Tong Shu Ozan Tugluk Hubertus van Dam Lipeng Wan Matthew Wolf Justin M. Wozniak Wei Xu Igor Yakushin Shinjae Yoo and Todd Munson. 2021. Online Data Analysis and Reduction: An Important Co-design Motif for Extreme-scale Computers. International Journal of High Performance Computing Applications (IJHPCA) (2021). Ian Foster Mark Ainsworth Julie Bessac Franck Cappello Jong Choi Sheng Di Zichao Di Ali M Gok Hanqi Guo Kevin A Huck Christopher Kelly Scott Klasky Kerstin Kleese van Dam Xin Liang Kshitij Mehta Manish Parashar Tom Peterka Line Pouchard Tong Shu Ozan Tugluk Hubertus van Dam Lipeng Wan Matthew Wolf Justin M. Wozniak Wei Xu Igor Yakushin Shinjae Yoo and Todd Munson. 2021. Online Data Analysis and Reduction: An Important Co-design Motif for Extreme-scale Computers. International Journal of High Performance Computing Applications (IJHPCA) (2021).","DOI":"10.1177\/10943420211023549"},{"key":"e_1_3_2_2_20_1","unstructured":"Geoffrey Fox Shantenu Jha and Lavanya Ramakrishnan. 2015. Streaming and Steering Applications: Requirements and Infrastructure final report. Geoffrey Fox Shantenu Jha and Lavanya Ramakrishnan. 2015. Streaming and Steering Applications: Requirements and Infrastructure final report."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Yuankun Fu Feng Li Fengguang Song and Zizhong Chen. 2018. Performance Analysis and Optimization of In-situ Integration of Simulation with Data Analysis: Zipping Applications Up. In ACM Intl. Symp. on High-Performance Parallel and Distributed Computing (HPDC). 192--205. Yuankun Fu Feng Li Fengguang Song and Zizhong Chen. 2018. Performance Analysis and Optimization of In-situ Integration of Simulation with Data Analysis: Zipping Applications Up. In ACM Intl. Symp. on High-Performance Parallel and Distributed Computing (HPDC). 192--205.","DOI":"10.1145\/3208040.3208049"},{"volume-title":"ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data (KDD). 1487--1496","year":"2017","author":"Golovin Daniel","key":"e_1_3_2_2_22_1"},{"key":"e_1_3_2_2_23_1","unstructured":"Heat Transfer. 2019. https:\/\/github.com\/CODARcode\/Example-Heat_Transfer\/blob\/master\/README.adoc. Heat Transfer. 2019. https:\/\/github.com\/CODARcode\/Example-Heat_Transfer\/blob\/master\/README.adoc."},{"volume-title":"Future Online Analysis Platform workshop report.","year":"2017","author":"Keahey Kate","key":"e_1_3_2_2_24_1"},{"key":"e_1_3_2_2_25_1","unstructured":"LAMMPS. 2021. https:\/\/lammps.sandia.gov. LAMMPS. 2021. https:\/\/lammps.sandia.gov."},{"volume-title":"IEEE\/ACM Intl. Conf. on High Performance Computing, Networking, Storage and Analysis (SC).","year":"2016","author":"Larsen Matthew","key":"e_1_3_2_2_26_1"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1002\/cpe.3125","article-title":"Hello ADIOS: the challenges and lessons of developing leadership class I\/O frameworks","volume":"26","author":"Liu Qing","year":"2014","journal-title":"Concurrency and Computation: Practice and Experience"},{"volume-title":"Optimal Scheduling of In-situ Analysis for Large-scale Scientific Simulations. In IEEE\/ACM Intl. Conf. on High Performance Computing, Networking, Storage and Analysis (SC)","author":"Malakar Preeti","key":"e_1_3_2_2_28_1"},{"volume-title":"Autotuning FPGA Design Parameters for Performance and Power. In IEEE Intl. Symp. on Field-Programmable Custom Computing Machines. 84--91","year":"2015","author":"Mametjanov Azamat","key":"e_1_3_2_2_29_1"},{"volume-title":"IEEE\/ACM Intl. Conf. on High Performance Computing, Networking, Storage and Analysis (SC). 1--12","year":"2017","author":"Marathe Aniruddha","key":"e_1_3_2_2_30_1"},{"volume-title":"ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP). 201--213","year":"2019","author":"Meng Ke","key":"e_1_3_2_2_31_1"},{"volume-title":"IEEE International Parallel and Distributed Processing Symposium (IPDPS). 831--840","year":"2020","author":"Menon Harshitha","key":"e_1_3_2_2_32_1"},{"volume-title":"ACM Intl. Conf. on Neural Information Processing Systems (NeurIPS). 1--11","year":"2019","author":"Morcos Ari","key":"e_1_3_2_2_33_1"},{"volume-title":"ACM\/IEEE Design Automation Conference (DAC). 1--6.","year":"2020","author":"Mu Jiandong","key":"e_1_3_2_2_34_1"},{"volume-title":"IEEE\/ACM Intl. Symp. on Code Generation and Optimization (CGO). 245--256","year":"2017","author":"Ogilvie William F.","key":"e_1_3_2_2_35_1"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1109\/TCSS.2018.2859189","article-title":"Extreme-Scale Dynamic Exploration of a Distributed Agent-Based Model with the EMEWS Framework","volume":"5","author":"Ozik Jonathan","year":"2018","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","DOI":"10.2172\/1571714","volume-title":"ASCR Workshop on In Situ Data Management report.","author":"Peterka Tom","year":"2019"},{"volume-title":"ACM International Conference on Supercomputing (ICS). 342--353","year":"2019","author":"Popov Mihail","key":"e_1_3_2_2_38_1"},{"volume-title":"Learning Cost-Effective Sampling Strategies for Empirical Performance Modeling. In IEEE International Parallel and Distributed Processing Symposium (IPDPS). 884--895","year":"2020","author":"Ritter Marcus","key":"e_1_3_2_2_39_1"},{"volume-title":"Performance Optimization and Energy Efficiency of Big-data Computing Workflows. Dissertation","year":"2010","author":"Shu Tong","key":"e_1_3_2_2_40_1"},{"volume-title":"Proc. of ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP). Virtual Event, 467--468","year":"2021","author":"Shu Tong","key":"e_1_3_2_2_41_1"},{"volume-title":"Proc. of Workshop on Workflows in Support of Large-Scale Science in conjunction with ACM\/IEEE Supercomputing Conference","year":"1800","author":"Shu Tong","key":"e_1_3_2_2_42_1"},{"volume-title":"Proc. of IEEE eScience","author":"Shu Tong","key":"e_1_3_2_2_43_1"},{"volume-title":"Proc. of IEEE INFOCOM","author":"Shu Tong","key":"e_1_3_2_2_44_1"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.future.2017.07.050","article-title":"Energy-efficient Mapping of Large-scale Workflows under Deadline Constraints in Big Data Computing Systems","volume":"110","author":"Shu Tong","year":"2020","journal-title":"Future Generation Computer Systems (FGCS)"},{"volume-title":"IEEE\/ACM Intl. Conf. on High Performance Computing, Networking, Storage and Analysis (SC).","year":"2017","author":"Sourouri Mohammed","key":"e_1_3_2_2_46_1"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"Rick Stevens Jeffrey Nichols and Katherine Yelick. 2020. AI for Science Report on the Department of Energy (DOE) Town Halls on Artificial Intelligence (AI) for Science. Rick Stevens Jeffrey Nichols and Katherine Yelick. 2020. AI for Science Report on the Department of Energy (DOE) Town Halls on Artificial Intelligence (AI) for Science.","DOI":"10.2172\/1604756"},{"volume-title":"Stacker: An Autonomic Data Movement Engine for Extreme-Scale Data Staging-Based In-Situ Workflows. In IEEE\/ACM Intl. Conf. on High Performance Computing, Networking, Storage and Analysis (SC).","year":"2018","author":"Subedi Pradeep","key":"e_1_3_2_2_48_1"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/TC.2020.2964767","article-title":"Automated Performance Modeling of HPC Applications Using Machine Learning","volume":"69","author":"Sun Jingwei","year":"2020","journal-title":"IEEE Trans. on Computers (TC)"},{"volume-title":"ACM International Conference on Supercomputing (ICS). 385--395","year":"2018","author":"Thiagarajan Jayaraman J.","key":"e_1_3_2_2_50_1"},{"volume-title":"IEEE\/ACM Intl. Conf. on High Performance Computing, Networking, Storage and Analysis (SC).","year":"2017","author":"Tillet Philippe","key":"e_1_3_2_2_51_1"},{"volume-title":"IEEE\/ACM Intl. Conf. on High Performance Computing, Networking, Storage and Analysis (SC).","author":"Vishwanath Venkatram","key":"e_1_3_2_2_52_1"},{"key":"e_1_3_2_2_53_1","unstructured":"Voro++. 2021. http:\/\/math.lbl.gov\/voro++. Voro++. 2021. http:\/\/math.lbl.gov\/voro++."},{"volume-title":"Proc. of the 4th Workshop on Machine Learning in HPC Environments in conjunction with ACM\/IEEE Supercomputing Conference","year":"2018","author":"Wozniak Justin M.","key":"e_1_3_2_2_54_1"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.future.2019.05.020","article-title":"MPI Jobs within MPI Jobs: a Practical Way of Enabling Task-level Fault-tolerance in HPC Workflows","volume":"101","author":"Wozniak Justin M.","year":"2019","journal-title":"Future Generation Computer Systems (FGCS)"},{"key":"e_1_3_2_2_56_1","first-page":"225","article-title":"A Boosted Decision Tree Approach using Bayesian Hyper-parameter Optimization for Credit Scoring","volume":"75","author":"Xia Yufei","year":"2017","journal-title":"Expert Systems with Applications"},{"volume-title":"ACM Intl. Conf. on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 564--577","year":"2018","author":"Yu Zhibin","key":"e_1_3_2_2_57_1"},{"key":"e_1_3_2_2_58_1","first-page":"1","article-title":"In-memory staging and data-centric task placement for coupled scientific simulation workflows","volume":"29","author":"Zhang Fan","year":"2017","journal-title":"Concurrency and Computation: Practice and Experience"},{"volume-title":"IEEE International Parallel and Distributed Processing Symposium (IPDPS). 320--331","year":"2013","author":"Zheng Fang","key":"e_1_3_2_2_59_1"}],"event":{"name":"SC '21: The International Conference for High Performance Computing, Networking, Storage and Analysis","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing","IEEE CS"],"location":"St. Louis Missouri","acronym":"SC '21"},"container-title":["Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3458817.3476197","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3458817.3476197","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:21Z","timestamp":1750191141000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3458817.3476197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,13]]},"references-count":59,"alternative-id":["10.1145\/3458817.3476197","10.1145\/3458817"],"URL":"https:\/\/doi.org\/10.1145\/3458817.3476197","relation":{},"subject":[],"published":{"date-parts":[[2021,11,13]]}}}