{"id":"https://openalex.org/W7159589810","doi":"https://doi.org/10.48550/arxiv.2604.27085","title":"Efficient Training on Multiple Consumer GPUs with RoundPipe","display_name":"Efficient Training on Multiple Consumer GPUs with RoundPipe","publication_year":2026,"publication_date":"2026-04-29","ids":{"openalex":"https://openalex.org/W7159589810","doi":"https://doi.org/10.48550/arxiv.2604.27085"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.27085","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27085","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2604.27085","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134992034","display_name":"Yibin Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Yibin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048518853","display_name":"Shiwei Gao","orcid":"https://orcid.org/0000-0001-9145-2309"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Shiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062619884","display_name":"Huichuan Zheng","orcid":"https://orcid.org/0000-0002-6669-969X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Huichuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134947636","display_name":"Youyou Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Youyou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134995712","display_name":"Jiwu Shu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shu, Jiwu","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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.7218999862670898,"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":0.7218999862670898,"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/T14347","display_name":"Big Data and Digital Economy","score":0.09179999679327011,"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"}},{"id":"https://openalex.org/T10101","display_name":"Cloud Computing and Resource Management","score":0.04190000146627426,"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/scheduling","display_name":"Scheduling (production processes)","score":0.6029999852180481},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5291000008583069},{"id":"https://openalex.org/keywords/compiler","display_name":"Compiler","score":0.4462999999523163},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4253999888896942},{"id":"https://openalex.org/keywords/pci-express","display_name":"PCI Express","score":0.4221000075340271},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.40529999136924744},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.38089999556541443},{"id":"https://openalex.org/keywords/interoperability","display_name":"Interoperability","score":0.36559998989105225},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.36410000920295715}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8902999758720398},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.625},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.6029999852180481},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5291000008583069},{"id":"https://openalex.org/C169590947","wikidata":"https://www.wikidata.org/wiki/Q47506","display_name":"Compiler","level":2,"score":0.4462999999523163},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4253999888896942},{"id":"https://openalex.org/C64270927","wikidata":"https://www.wikidata.org/wiki/Q206924","display_name":"PCI Express","level":3,"score":0.4221000075340271},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.40529999136924744},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C20136886","wikidata":"https://www.wikidata.org/wiki/Q749647","display_name":"Interoperability","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.36410000920295715},{"id":"https://openalex.org/C2778514511","wikidata":"https://www.wikidata.org/wiki/Q1374194","display_name":"Programmer","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.34689998626708984},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.3463999927043915},{"id":"https://openalex.org/C43364308","wikidata":"https://www.wikidata.org/wiki/Q8799","display_name":"Byte","level":2,"score":0.34540000557899475},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3199999928474426},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.3151000142097473},{"id":"https://openalex.org/C63000827","wikidata":"https://www.wikidata.org/wiki/Q3080428","display_name":"Software portability","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C2778562939","wikidata":"https://www.wikidata.org/wiki/Q1298791","display_name":"Synchronization (alternating current)","level":3,"score":0.2802000045776367},{"id":"https://openalex.org/C67953723","wikidata":"https://www.wikidata.org/wiki/Q192525","display_name":"Workstation","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C55416958","wikidata":"https://www.wikidata.org/wiki/Q6206757","display_name":"Job shop scheduling","level":3,"score":0.2639999985694885},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C34165917","wikidata":"https://www.wikidata.org/wiki/Q188267","display_name":"Programming paradigm","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C106251023","wikidata":"https://www.wikidata.org/wiki/Q851989","display_name":"Porting","level":3,"score":0.26109999418258667},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.26100000739097595}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.27085","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27085","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2604.27085","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27085","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fine-tuning":[0],"Large":[1],"Language":[2],"Models":[3],"(LLMs)":[4],"on":[5,95,151,185],"consumer-grade":[6],"GPUs":[7,60,101],"is":[8,57,190],"highly":[9],"cost-effective,":[10],"yet":[11],"constrained":[12],"by":[13,31],"limited":[14],"GPU":[15,69,97],"memory":[16],"and":[17,109,128,144],"slow":[18],"PCIe":[19],"interconnects.":[20],"Pipeline":[21],"parallelism":[22],"combined":[23],"with":[24,70,181,198],"CPU":[25],"offloading":[26],"mitigates":[27],"these":[28],"hardware":[29],"bottlenecks":[30],"reducing":[32],"communication":[33],"overhead.":[34],"However,":[35],"existing":[36],"PP":[37],"schedules":[38],"suffer":[39],"from":[40],"an":[41,145,152,194],"inherent":[42],"limitation":[43],"termed":[44],"the":[45,54,62,68,71,91,178],"weight":[46,92],"binding":[47,93],"issue.":[48],"Binding":[49],"uneven":[50],"model":[51,180],"stages":[52,113],"(e.g.,":[53],"LM":[55],"head":[56],"large)":[58],"to":[59,65,75,169],"limits":[61],"pipeline's":[63],"throughput":[64],"that":[66,89,158],"of":[67,105,177],"heaviest":[72],"load,":[73],"leading":[74],"severe":[76],"pipeline":[77,87],"bubbles.":[78],"In":[79],"this":[80],"paper,":[81],"we":[82],"propose":[83],"RoundPipe,":[84],"a":[85,103,117,121,133,138,186],"novel":[86],"schedule":[88],"breaks":[90],"constraint":[94],"consumer":[96],"servers.":[98],"RoundPipe":[99,131,159,173,189],"treats":[100],"as":[102,193],"pool":[104],"stateless":[106],"execution":[107],"workers":[108],"dynamically":[110],"dispatches":[111],"computation":[112],"across":[114],"devices":[115],"in":[116],"round-robin":[118],"manner,":[119],"achieving":[120],"near-zero-bubble":[122],"pipeline.":[123],"To":[124],"ensure":[125],"training":[126],"correctness":[127],"system":[129],"efficiency,":[130],"integrates":[132],"priority-aware":[134],"transfer":[135],"scheduling":[136],"engine,":[137],"fine-grained":[139],"distributed":[140],"event-based":[141],"synchronization":[142],"protocol,":[143],"automated":[146],"layer":[147],"partitioning":[148],"algorithm.":[149],"Evaluations":[150],"8$\\times$":[153],"RTX":[154],"4090":[155],"server":[156],"demonstrate":[157],"achieves":[160],"1.48--2.16$\\times$":[161],"speedups":[162],"over":[163],"state-of-the-art":[164],"baselines":[165],"when":[166],"fine-tuning":[167,176],"1.7B":[168],"32B":[170],"models.":[171],"Remarkably,":[172],"enables":[174],"LoRA":[175],"Qwen3-235B":[179],"31K":[182],"sequence":[183],"length":[184],"single":[187],"server.":[188],"publicly":[191],"available":[192],"open-source":[195],"Python":[196],"library":[197],"comprehensive":[199],"documentation.":[200]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-02T00:00:00"}