{"id":"https://openalex.org/W7161012755","doi":"https://doi.org/10.48550/arxiv.2605.11335","title":"ChunkFlow: Communication-Aware Chunked Prefetching for Layerwise Offloading in Distributed Diffusion Transformer Inference","display_name":"ChunkFlow: Communication-Aware Chunked Prefetching for Layerwise Offloading in Distributed Diffusion Transformer Inference","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7161012755","doi":"https://doi.org/10.48550/arxiv.2605.11335"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.11335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11335","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.2605.11335","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136046031","display_name":"Han Meng","orcid":null},"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":"Meng, Han","raw_affiliation_strings":["University of California, Merced"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Merced","institution_ids":["https://openalex.org/I156087764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130577415","display_name":"Danny Willow Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liu, Danny Willow","raw_affiliation_strings":["University of Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chicago","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5136047329","display_name":"Dong Li","orcid":null},"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":"Li, Dong","raw_affiliation_strings":["University of California, Merced,Yotta Labs"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Merced,Yotta Labs","institution_ids":["https://openalex.org/I156087764"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":0.4092000126838684,"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":0.4092000126838684,"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":0.1462000012397766,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.11150000244379044,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/instruction-prefetch","display_name":"Instruction prefetch","score":0.968500018119812},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5737000107765198},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.5461999773979187},{"id":"https://openalex.org/keywords/pci-express","display_name":"PCI Express","score":0.5351999998092651},{"id":"https://openalex.org/keywords/dram","display_name":"Dram","score":0.5159000158309937},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.5067999958992004},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4862000048160553},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.44780001044273376}],"concepts":[{"id":"https://openalex.org/C133588205","wikidata":"https://www.wikidata.org/wiki/Q28455645","display_name":"Instruction prefetch","level":3,"score":0.968500018119812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8108999729156494},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5737000107765198},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.5461999773979187},{"id":"https://openalex.org/C64270927","wikidata":"https://www.wikidata.org/wiki/Q206924","display_name":"PCI Express","level":3,"score":0.5351999998092651},{"id":"https://openalex.org/C7366592","wikidata":"https://www.wikidata.org/wiki/Q1255620","display_name":"Dram","level":2,"score":0.5159000158309937},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.5067999958992004},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4862000048160553},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.45840001106262207},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.44780001044273376},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.41760000586509705},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3714999854564667},{"id":"https://openalex.org/C189930140","wikidata":"https://www.wikidata.org/wiki/Q1112878","display_name":"CAS latency","level":4,"score":0.33480000495910645},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3303000032901764},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.30970001220703125},{"id":"https://openalex.org/C183373512","wikidata":"https://www.wikidata.org/wiki/Q949618","display_name":"Taxis","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.29809999465942383},{"id":"https://openalex.org/C130795937","wikidata":"https://www.wikidata.org/wiki/Q2561570","display_name":"Remote direct memory access","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C169590947","wikidata":"https://www.wikidata.org/wiki/Q47506","display_name":"Compiler","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C188045654","wikidata":"https://www.wikidata.org/wiki/Q17148339","display_name":"Memory bandwidth","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C204495577","wikidata":"https://www.wikidata.org/wiki/Q1205349","display_name":"Callback","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.11335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11335","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.2605.11335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11335","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":{"Layerwise":[0],"offloading":[1,75,109],"reduces":[2,153],"the":[3,36,58,162,170,187],"GPU":[4,120,155],"memory":[5,121,156],"footprint":[6],"of":[7],"large":[8,173],"diffusion":[9,128],"transformer":[10],"(DiT)":[11],"inference":[12],"by":[13,85,97,157],"prefetching":[14],"upcoming":[15],"layers":[16],"from":[17],"host":[18],"memory,":[19],"but":[20],"its":[21],"effectiveness":[22],"hinges":[23],"on":[24,43,57,100,131],"hiding":[25],"prefetch":[26,46,63,81,93,123],"latency":[27,64],"behind":[28],"per-layer":[29],"computation.":[30,98],"This":[31],"assumption":[32],"breaks":[33],"down":[34],"when":[35,66,92],"per-GPU":[37],"compute":[38,67],"workload":[39,171],"is":[40,172],"small.":[41],"Moreover,":[42],"PCIe-only":[44],"nodes,":[45],"and":[47,54,82,117,175],"inter-GPU":[48],"collective":[49,115],"communications":[50],"such":[51],"as":[52,76],"all-reduce":[53],"all-to-all":[55],"contend":[56],"shared":[59],"PCIe":[60,136],"path,":[61],"exposing":[62],"even":[65],"would":[68],"otherwise":[69],"hide":[70],"it.":[71],"We":[72],"revisit":[73],"layerwise":[74,151],"a":[77,86,106,177],"co-scheduling":[78],"problem":[79],"between":[80],"communication,":[83],"guided":[84],"first-order":[87],"analytical":[88],"model":[89],"that":[90,111,181],"predicts":[91],"can":[94],"be":[95],"hidden":[96],"Building":[99],"this":[101],"model,":[102],"we":[103],"design":[104],"ChunkFlow,":[105],"communication-aware,":[107],"chunk-granular":[108],"runtime":[110],"adaptively":[112],"yields":[113],"to":[114,144,159],"communication":[116],"smoothly":[118],"trades":[119],"for":[122],"volume.":[124],"On":[125],"three":[126],"representative":[127],"transformers":[129],"running":[130],"two":[132],"H100":[133],"GPUs":[134],"over":[135,148,161],"with":[137],"Ulysses":[138],"sequence":[139],"parallelism,":[140],"ChunkFlow":[141],"delivers":[142],"up":[143,158],"1.28x":[145],"step-time":[146,184],"speedup":[147],"SGLang's":[149],"existing":[150],"offloading,":[152],"peak":[154],"49%":[160],"no-offload":[163],"baseline":[164],"at":[165],"near-identical":[166],"step":[167],"time":[168],"once":[169],"enough,":[174],"exposes":[176],"tunable":[178],"memory-latency":[179],"tradeoff":[180],"recovers":[182],"near-zero":[183],"overhead":[185],"in":[186],"small-workload":[188],"regime.":[189]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-14T00:00:00"}