{"id":"https://openalex.org/W4284958556","doi":"https://doi.org/10.1145/3491418.3535172","title":"Towards Practical, Generalizable Machine-Learning Training Pipelines to build Regression Models for Predicting Application Resource Needs on HPC Systems","display_name":"Towards Practical, Generalizable Machine-Learning Training Pipelines to build Regression Models for Predicting Application Resource Needs on HPC Systems","publication_year":2022,"publication_date":"2022-07-08","ids":{"openalex":"https://openalex.org/W4284958556","doi":"https://doi.org/10.1145/3491418.3535172"},"language":"en","primary_location":{"id":"doi:10.1145/3491418.3535172","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3491418.3535172","pdf_url":null,"source":{"id":"https://openalex.org/S4306523034","display_name":"Practice and Experience in Advanced Research Computing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Practice and Experience in Advanced Research Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036221367","display_name":"Manikya Swathi Vallabhajosyula","orcid":"https://orcid.org/0000-0002-9094-1722"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Manikya Swathi Vallabhajosyula","raw_affiliation_strings":["Computer Science and Engineering, The Ohio State University, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, The Ohio State University, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073535794","display_name":"Rajiv Ramnath","orcid":"https://orcid.org/0000-0003-0093-8560"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rajiv Ramnath","raw_affiliation_strings":["Computer Science and Engineering, The Ohio State University, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, The Ohio State University, USA","institution_ids":["https://openalex.org/I52357470"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5036221367"],"corresponding_institution_ids":["https://openalex.org/I52357470"],"apc_list":null,"apc_paid":null,"fwci":0.9085,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.71202532,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11181","display_name":"Advanced Data Storage Technologies","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10101","display_name":"Cloud Computing and Resource Management","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8098908066749573},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7800182104110718},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.731226921081543},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6725793480873108},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5791746973991394},{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.571724534034729},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5406901240348816},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.5081110000610352},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4742652177810669},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.46188682317733765},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4426501393318176},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4260272681713104},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3525032103061676},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.18062660098075867},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09265932440757751}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8098908066749573},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7800182104110718},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.731226921081543},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6725793480873108},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5791746973991394},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.571724534034729},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5406901240348816},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.5081110000610352},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4742652177810669},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.46188682317733765},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4426501393318176},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4260272681713104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3525032103061676},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.18062660098075867},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09265932440757751},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C87717796","wikidata":"https://www.wikidata.org/wiki/Q146326","display_name":"Environmental engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3491418.3535172","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3491418.3535172","pdf_url":null,"source":{"id":"https://openalex.org/S4306523034","display_name":"Practice and Experience in Advanced Research Computing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Practice and Experience in Advanced Research Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1966275653","https://openalex.org/W2051385358","https://openalex.org/W2074053825","https://openalex.org/W2131271579","https://openalex.org/W2136434791","https://openalex.org/W2559932280","https://openalex.org/W2999472483"],"related_works":["https://openalex.org/W1995889332","https://openalex.org/W3104163240","https://openalex.org/W3008693296","https://openalex.org/W4312283151","https://openalex.org/W1995617853","https://openalex.org/W4285322112","https://openalex.org/W3158596343","https://openalex.org/W4292794239","https://openalex.org/W4385572030","https://openalex.org/W4282583532"],"abstract_inverted_index":{"This":[0,30],"paper":[1],"explores":[2],"the":[3,16,72,80,97,105,111],"potential":[4],"for":[5,14,58,103],"cost-effectively":[6],"developing":[7],"generalizable":[8],"and":[9,28,47,69,84,95,109],"scalable":[10],"machine-learning-based":[11],"regression":[12],"models":[13,37,56],"predicting":[15],"approximate":[17],"execution":[18],"time":[19],"of":[20,82,89,99],"an":[21,100],"HPC":[22],"application":[23],"given":[24],"its":[25,85],"input":[26],"data":[27,83,108],"parameters.":[29],"work":[31],"examines:":[32],"(a)":[33],"to":[34,49,53,63],"what":[35,54],"extent":[36,55],"can":[38,61],"be":[39],"trained":[40],"on":[41,44,79],"scaled-down":[42],"datasets":[43],"commodity":[45],"environments":[46],"adapted":[48],"production":[50],"environments,":[51],"(b)":[52],"built":[57],"specific":[59],"applications":[60,65],"generalize":[62],"other":[64],"within":[66],"a":[67],"family,":[68],"(c)":[70],"how":[71],"most":[73],"appropriate":[74],"model":[75],"may":[76],"change":[77],"based":[78],"type":[81],"mix.":[86],"As":[87],"part":[88],"this":[90],"work,":[91],"we":[92],"also":[93],"describe":[94],"show":[96],"use":[98],"automatable":[101],"pipeline":[102],"generating":[104],"necessary":[106],"training":[107],"building":[110],"model.":[112]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
