{"id":"https://openalex.org/W7159059114","doi":"https://doi.org/10.48550/arxiv.2604.26687","title":"COPUS: Co-adaptive Parallelism and Batch Size Selection in Large Language Model Training","display_name":"COPUS: Co-adaptive Parallelism and Batch Size Selection in Large Language Model Training","publication_year":2026,"publication_date":"2026-04-29","ids":{"openalex":"https://openalex.org/W7159059114","doi":"https://doi.org/10.48550/arxiv.2604.26687"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.26687","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26687","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.26687","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106361399","display_name":"Akhmed Sakip","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sakip, Akhmed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017355779","display_name":"Erland Hilman Fuadi","orcid":"https://orcid.org/0000-0002-9569-2884"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fuadi, Erland Hilman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134916304","display_name":"Omar Sayedelahl","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sayedelahl, Omar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022284072","display_name":"Zonghang Li","orcid":"https://orcid.org/0000-0002-2796-039X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zonghang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134926893","display_name":"Jianshu She","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"She, Jianshu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134885176","display_name":"Alham Fikri Aji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aji, Alham Fikri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134882489","display_name":"Steve Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Steve","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134917155","display_name":"Eric Xing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xing, Eric","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134879850","display_name":"Qirong Ho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ho, Qirong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.3752000033855438,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.3752000033855438,"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/T10028","display_name":"Topic Modeling","score":0.1354999989271164,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.043800000101327896,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/parallelism","display_name":"Parallelism (grammar)","score":0.6675999760627747},{"id":"https://openalex.org/keywords/data-parallelism","display_name":"Data parallelism","score":0.6305000185966492},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.4878000020980835},{"id":"https://openalex.org/keywords/batch-processing","display_name":"Batch processing","score":0.4169999957084656},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.3490000069141388},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.3479999899864197},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.33730000257492065},{"id":"https://openalex.org/keywords/control-reconfiguration","display_name":"Control reconfiguration","score":0.3294000029563904}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8156999945640564},{"id":"https://openalex.org/C2781172179","wikidata":"https://www.wikidata.org/wiki/Q853109","display_name":"Parallelism (grammar)","level":2,"score":0.6675999760627747},{"id":"https://openalex.org/C61483411","wikidata":"https://www.wikidata.org/wiki/Q3124522","display_name":"Data parallelism","level":3,"score":0.6305000185966492},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.5404000282287598},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.4878000020980835},{"id":"https://openalex.org/C172658912","wikidata":"https://www.wikidata.org/wiki/Q661613","display_name":"Batch processing","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C119701452","wikidata":"https://www.wikidata.org/wiki/Q5165881","display_name":"Control reconfiguration","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C106515295","wikidata":"https://www.wikidata.org/wiki/Q26806595","display_name":"Parallel processing","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3061000108718872},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2619999945163727},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C107027933","wikidata":"https://www.wikidata.org/wiki/Q2006448","display_name":"Stream processing","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C42992933","wikidata":"https://www.wikidata.org/wiki/Q691169","display_name":"Task parallelism","level":3,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.26687","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26687","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.26687","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26687","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Training":[0],"large":[1],"language":[2],"models":[3,149],"requires":[4],"jointly":[5,155],"configuring":[6],"two":[7],"interdependent":[8],"aspects":[9],"of":[10,112,143,163,180,187,205,222],"the":[11,13,22,39,44,57,69,83,90,103,113,124,141,225],"system:":[12],"global":[14,91,125],"batch":[15,40,47,64,71,92,126,189],"size,":[16,127],"which":[17,26,148],"governs":[18,27],"statistical":[19,146,153],"efficiency,":[20,147],"and":[21,53,130,145,152,156,183,191,207,210],"3D":[23,175],"parallelism":[24,51,59,85,128,176,192],"strategy,":[25,129],"hardware":[28,151],"throughput.":[29],"Existing":[30],"approaches":[31],"make":[32],"these":[33,78],"decisions":[34,79],"independently:":[35],"optimization":[36],"work":[37,55],"adapts":[38],"size":[41,48,65,72,93,132,190],"to":[42,215,235],"track":[43],"evolving":[45],"critical":[46],"while":[49,101],"keeping":[50],"fixed,":[52],"systems":[54],"selects":[56],"fastest":[58,226],"for":[60,110],"a":[61,107,119],"given":[62],"fixed":[63],"without":[66],"anticipating":[67],"that":[68,77,98,121],"optimal":[70],"could":[73],"change.":[74],"We":[75,116,195],"show":[76],"are":[80],"tightly":[81],"coupled:":[82],"throughput-optimal":[84],"strategy":[86],"may":[87],"shift":[88],"as":[89,133],"changes,":[94],"so":[95],"any":[96],"method":[97],"fixes":[99],"one":[100],"adapting":[102],"other":[104],"operates":[105],"with":[106,177,231],"suboptimal":[108],"configuration":[109],"part":[111],"training":[114,134],"run.":[115],"present":[117],"COPUS,":[118],"system":[120,167,238],"adaptively":[122],"tunes":[123],"micro-batch":[131],"evolves.":[135],"COPUS":[136,197],"is":[137],"guided":[138],"by":[139],"Goodput,":[140],"product":[142],"throughput":[144],"both":[150,188],"effects":[154],"directly":[157],"measures":[158],"useful":[159],"convergence":[160],"per":[161],"unit":[162],"wall-clock":[164],"time.":[165],"The":[166],"combines":[168],"online":[169],"gradient":[170],"noise":[171],"scale":[172],"estimation":[173],"under":[174],"throughput-aware":[178],"evaluation":[179],"candidate":[181],"configurations,":[182,230],"supports":[184],"efficient":[185],"reconfiguration":[186],"during":[193],"training.":[194],"evaluate":[196],"on":[198],"LLM":[199],"pre-training":[200],"workloads":[201],"across":[202,228],"1-4":[203],"nodes":[204],"8xH100":[206],"8xMI210":[208],"GPUs":[209],"model":[211],"sizes":[212],"from":[213],"3B":[214],"32B":[216],"parameters,":[217],"demonstrating":[218],"average":[219],"time-to-convergence":[220],"speedups":[221],"3.9-8.0%":[223],"over":[224],"baseline":[227],"four":[229],"peak":[232],"gains":[233],"up":[234],"11.1%,":[236],"including":[237],"overheads.":[239]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-01T00:00:00"}