{"id":"https://openalex.org/W7139113455","doi":"https://doi.org/10.48550/arxiv.2603.15854","title":"FlashSampling: Fast and Memory-Efficient Exact Sampling","display_name":"FlashSampling: Fast and Memory-Efficient Exact Sampling","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7139113455","doi":"https://doi.org/10.48550/arxiv.2603.15854"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.15854","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15854","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.2603.15854","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130107360","display_name":"Tomas Ruiz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruiz, Tomas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129888906","display_name":"Zhen Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Zhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130090202","display_name":"Yifan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063135412","display_name":"Xuyang Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Xuyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130151894","display_name":"Yiran Zhong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Yiran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129976732","display_name":"Mengdi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Mengdi","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":0.2946000099182129,"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":0.2946000099182129,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.07840000092983246,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.06560000032186508,"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/sampling","display_name":"Sampling (signal processing)","score":0.6148999929428101},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5264999866485596},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.49810001254081726},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.35910001397132874},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.3456000089645386},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.3370000123977661},{"id":"https://openalex.org/keywords/poisson-sampling","display_name":"Poisson sampling","score":0.3176000118255615}],"concepts":[{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6148999929428101},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5529000163078308},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5529000163078308},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5264999866485596},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.49810001254081726},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44200000166893005},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3630000054836273},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.35910001397132874},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.3456000089645386},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C82152865","wikidata":"https://www.wikidata.org/wiki/Q7208505","display_name":"Poisson sampling","level":5,"score":0.3176000118255615},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.29249998927116394},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.28619998693466187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2854999899864197},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C190190378","wikidata":"https://www.wikidata.org/wiki/Q1192625","display_name":"Process capability index","level":3,"score":0.26260000467300415},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2574999928474426},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25519999861717224},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.15854","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15854","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.2603.15854","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15854","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sampling":[0],"from":[1],"a":[2,72],"categorical":[3,134],"distribution":[4],"is":[5,49,113],"mathematically":[6],"simple,":[7],"but":[8],"in":[9,45,153,193],"large-vocabulary":[10],"decoding,":[11,79],"it":[12,157],"often":[13],"triggers":[14],"extra":[15,19],"memory":[16],"traffic":[17],"and":[18,39,65,69,96,124,152],"kernels":[20],"after":[21],"the":[22,36,42,82,133,168,185,189],"LM":[23],"head.":[24],"We":[25],"present":[26],"FlashSampling,":[27],"an":[28,194],"exact":[29,114,128,176],"sampling":[30,34,191],"primitive":[31],"that":[32,175],"fuses":[33],"into":[35,184],"LM-head":[37],"matmul":[38,186],"never":[40],"materializes":[41],"logits":[43,52,85],"tensor":[44],"HBM.":[46],"The":[47],"method":[48],"simple:":[50],"compute":[51],"tile-by-tile":[53],"on":[54,140,167],"chip,":[55],"add":[56],"Gumbel":[57],"noise,":[58],"keep":[59],"only":[60],"one":[61],"maximizer":[62],"per":[63,66,160],"row":[64],"vocabulary":[67],"tile,":[68],"finish":[70],"with":[71,86,94,104,178],"small":[73],"reduction":[74],"over":[75,118],"tiles.":[76],"In":[77],"tensor-parallel":[78,125],"FlashSampling":[80,136],"replaces":[81],"all-gather":[83],"of":[84,132],"streaming":[87],"peer-to-peer":[88],"writes:":[89],"This":[90],"overlaps":[91],"GPU-to-GPU":[92],"communication":[93],"computation":[95],"HBM":[97],"loads":[98],"across":[99,143],"up":[100,164],"to":[101,165],"8":[102],"GPUs,":[103],"near-ideal":[105],"scaling":[106],"at":[107],"large":[108],"batch":[109],"sizes.":[110],"Our":[111],"kernel":[112],"because":[115],"argmax":[116],"decomposes":[117],"partitions;":[119],"grouped":[120],"variants":[121],"for":[122],"online":[123],"settings":[126],"are":[127],"by":[129,163],"hierarchical":[130],"factorization":[131],"distribution.":[135],"demonstrates":[137],"kernel-level":[138],"speedups":[139],"decode":[141],"workloads":[142],"4":[144],"different":[145],"datacenter":[146],"GPUs":[147],"(H100,":[148],"H200,":[149],"B200,":[150],"B300),":[151],"end-to-end":[154],"vLLM":[155],"experiments,":[156],"reduces":[158],"time":[159],"output":[161],"token":[162],"$10\\%$":[166],"models":[169],"we":[170],"test.":[171],"These":[172],"results":[173],"show":[174],"sampling,":[177],"no":[179],"approximation,":[180],"can":[181],"be":[182],"integrated":[183],"itself,":[187],"consolidating":[188],"bandwidth-bound":[190],"step":[192],"efficient":[195],"epilogue.":[196]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-20T00:00:00"}