{"id":"https://openalex.org/W4412610602","doi":"https://doi.org/10.1109/hpcc64274.2024.00084","title":"CPU Power Modeling Through Training Data Selection","display_name":"CPU Power Modeling Through Training Data Selection","publication_year":2024,"publication_date":"2024-12-13","ids":{"openalex":"https://openalex.org/W4412610602","doi":"https://doi.org/10.1109/hpcc64274.2024.00084"},"language":"en","primary_location":{"id":"doi:10.1109/hpcc64274.2024.00084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpcc64274.2024.00084","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on High Performance Computing and Communications (HPCC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5106482041","display_name":"Zekai Li","orcid":"https://orcid.org/0009-0008-3962-6074"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zekai Li","raw_affiliation_strings":["National University of Defense Technology,College of Computer Science and Technology,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,College of Computer Science and Technology,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiaqing Zhong","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqing Zhong","raw_affiliation_strings":["National University of Defense Technology,College of Computer Science and Technology,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,College of Computer Science and Technology,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006736541","display_name":"Juan Chen","orcid":"https://orcid.org/0000-0002-9941-9885"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juan Chen","raw_affiliation_strings":["National University of Defense Technology,College of Computer Science and Technology,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,College of Computer Science and Technology,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37829708,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"584","last_page":"593"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.5216000080108643,"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":0.5216000080108643,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7136144638061523},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6708911061286926},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6577722430229187},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.5553671717643738},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4366210997104645},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3161618113517761},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1867256760597229}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7136144638061523},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6708911061286926},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6577722430229187},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.5553671717643738},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4366210997104645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3161618113517761},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1867256760597229},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hpcc64274.2024.00084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpcc64274.2024.00084","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on High Performance Computing and Communications (HPCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2001822216","https://openalex.org/W2002950519","https://openalex.org/W2009653610","https://openalex.org/W2031867463","https://openalex.org/W2069590084","https://openalex.org/W2070310479","https://openalex.org/W2143011780","https://openalex.org/W2156273182","https://openalex.org/W2215983828","https://openalex.org/W2287115896","https://openalex.org/W2422691685","https://openalex.org/W2484473840","https://openalex.org/W2750846724","https://openalex.org/W2931147495","https://openalex.org/W2937791656","https://openalex.org/W3100406893","https://openalex.org/W3170871280","https://openalex.org/W3206406968","https://openalex.org/W4242123872","https://openalex.org/W4247546971","https://openalex.org/W4255432895","https://openalex.org/W4256470818","https://openalex.org/W4285326518","https://openalex.org/W4290079642","https://openalex.org/W4393319739","https://openalex.org/W4396782867","https://openalex.org/W6675121713","https://openalex.org/W6852449896","https://openalex.org/W6853775986","https://openalex.org/W6860362861"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W4366179611","https://openalex.org/W2996078371"],"abstract_inverted_index":{"CPU":[0,43],"power":[1,9,13,16,30,44],"modeling":[2,45],"aims":[3],"to":[4,8,58],"address":[5],"issues":[6],"related":[7],"prediction":[10],"and":[11,65,72,97],"fine-grained":[12],"measurement":[14],"during":[15],"management.":[17],"However,":[18],"existing":[19],"studies":[20],"have":[21],"mainly":[22],"focused":[23],"on":[24,70],"designing":[25],"supervised":[26],"learning":[27],"methods":[28],"for":[29,88,93,99],"modeling,":[31],"neglecting":[32],"the":[33,79,83,89,94,100],"effect":[34],"of":[35],"training":[36],"samples.":[37],"Therefore,":[38],"this":[39],"paper":[40],"presents":[41],"a":[42],"method":[46,55,80],"that":[47,78],"adaptively":[48],"selects":[49],"high-quality":[50],"sample":[51],"data":[52],"iteratively.":[53],"The":[54],"is":[56],"applied":[57],"linear":[59],"regression":[60],"(LR),":[61],"neural":[62],"networks":[63],"(NN)":[64],"random":[66],"forest":[67],"models":[68],"(RF)":[69],"x86":[71],"ARM-based":[73],"platforms.":[74],"Experimental":[75],"results":[76],"show":[77],"can":[81],"reduce":[82],"average":[84],"MAPE":[85],"by":[86],"13.33%":[87],"LR":[90],"model,":[91],"27.45%":[92],"NN":[95],"model":[96],"28.30%":[98],"RF":[101],"model.":[102]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}