{"id":"https://openalex.org/W4416199161","doi":"https://doi.org/10.1145/3712285.3759827","title":"HPC-R1: Characterizing R1-like Large Reasoning Models on HPC","display_name":"HPC-R1: Characterizing R1-like Large Reasoning Models on HPC","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W4416199161","doi":"https://doi.org/10.1145/3712285.3759827"},"language":null,"primary_location":{"id":"doi:10.1145/3712285.3759827","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3712285.3759827","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3712285.3759827","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020106574","display_name":"Adam Weingram","orcid":"https://orcid.org/0009-0004-0027-6549"},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Adam Weingram","raw_affiliation_strings":["University of California, Merced, Merced, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Merced, Merced, USA","institution_ids":["https://openalex.org/I156087764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034730005","display_name":"Duo Zhang","orcid":"https://orcid.org/0009-0001-7700-2798"},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Duo Zhang","raw_affiliation_strings":["University of California, Merced, Merced, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Merced, Merced, USA","institution_ids":["https://openalex.org/I156087764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108600956","display_name":"Z. Chen","orcid":"https://orcid.org/0009-0004-1522-7686"},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhonghao Chen","raw_affiliation_strings":["University of California, Merced, Merced, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Merced, Merced, USA","institution_ids":["https://openalex.org/I156087764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091038854","display_name":"Hao Qi","orcid":"https://orcid.org/0009-0007-8795-5262"},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao Qi","raw_affiliation_strings":["University of California, Merced, Merced, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Merced, Merced, USA","institution_ids":["https://openalex.org/I156087764"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067748041","display_name":"Xiaoyi Lu","orcid":"https://orcid.org/0000-0001-7581-8905"},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoyi Lu","raw_affiliation_strings":["University of California, Merced, Merced, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Merced, Merced, USA","institution_ids":["https://openalex.org/I156087764"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5020106574"],"corresponding_institution_ids":["https://openalex.org/I156087764"],"apc_list":null,"apc_paid":null,"fwci":6.9946,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.96863032,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1368","last_page":"1380"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.1573999971151352,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10028","display_name":"Topic Modeling","score":0.1573999971151352,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.14259999990463257,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.10920000076293945,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/key","display_name":"Key (lock)","score":0.7768999934196472},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.7214999794960022},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5059999823570251},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.319599986076355},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3082999885082245}],"concepts":[{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.7768999934196472},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7623000144958496},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.7214999794960022},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5756000280380249},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5059999823570251},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4997999966144562},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3319000005722046},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.2971999943256378},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3712285.3759827","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3712285.3759827","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3712285.3759827","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3712285.3759827","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5156569437","display_name":null,"funder_award_id":"DE-SC0024207","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G7286037082","display_name":null,"funder_award_id":"2321123","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1596936080","https://openalex.org/W1965555277","https://openalex.org/W2033787161","https://openalex.org/W2081612620","https://openalex.org/W2736601468","https://openalex.org/W3129831491","https://openalex.org/W4311991106","https://openalex.org/W4362566357","https://openalex.org/W4366999304","https://openalex.org/W4387321091","https://openalex.org/W4387828615","https://openalex.org/W4391341581","https://openalex.org/W4391631327","https://openalex.org/W4405903187","https://openalex.org/W4406779522","https://openalex.org/W4407130444","https://openalex.org/W4412886857","https://openalex.org/W4416036482"],"related_works":[],"abstract_inverted_index":{"Large":[0],"Reasoning":[1],"Models":[2],"(LRMs)":[3],"are":[4],"becoming":[5],"increasingly":[6],"popular":[7],"as":[8],"they":[9],"offer":[10],"advanced":[11],"capabilities":[12],"in":[13,36],"logical":[14],"inference,":[15],"mathematical":[16],"reasoning,":[17],"and":[18,39,82,96,122],"knowledge":[19],"synthesis,":[20],"even":[21],"beyond":[22],"those":[23],"of":[24,49],"standard":[25],"language":[26],"models.":[27],"However,":[28],"their":[29],"complex":[30],"training":[31,51],"workflows":[32],"present":[33,104,131],"significant":[34],"challenges":[35],"reproducibility,":[37],"efficiency,":[38],"system-level":[40],"optimization.":[41],"This":[42],"paper":[43],"introduces":[44],"HPC-R1,":[45],"a":[46,60],"comprehensive":[47],"characterization":[48],"LRM":[50],"on":[52,59,127],"the":[53],"NERSC":[54],"Perlmutter":[55],"supercomputer,":[56],"representing":[57],"behavior":[58],"Top500-ranked":[61],"system.":[62],"We":[63],"analyze":[64],"all":[65,109],"major":[66],"stages,":[67,110],"including":[68,111],"supervised":[69],"fine-tuning":[70],"(SFT),":[71],"Group":[72],"Relative":[73],"Policy":[74],"Optimization":[75],"(GRPO)-based":[76],"reinforcement":[77],"learning":[78],"(RL),":[79],"autoregressive":[80],"generation,":[81,121],"distillation":[83],"using":[84],"customized":[85],"state-of-the-art":[86],"frameworks.":[87],"Our":[88],"detailed":[89],"performance":[90],"analysis":[91],"reveals":[92],"key":[93,106,133],"system":[94,139],"inefficiencies":[95],"scaling":[97],"behaviors.":[98],"Through":[99],"our":[100],"in-depth":[101],"analysis,":[102],"we":[103,130],"19":[105],"observations":[107],"across":[108],"4":[112],"for":[113,116,120,124],"SFT,":[114],"7":[115],"GRPO-based":[117],"RL,":[118],"6":[119],"2":[123],"distillation.":[125],"Based":[126],"these":[128],"findings,":[129],"several":[132],"recommendations":[134],"to":[135],"guide":[136],"future":[137],"HPC-AI":[138],"design.":[140]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-11T08:14:18.477133","created_date":"2025-11-12T00:00:00"}
