{"id":"https://openalex.org/W7204885596","doi":"https://doi.org/10.48550/arxiv.2608.30239","title":"GPU-Parallelization of Markov Chain Pool Decoding with Unbiased MCMC","display_name":"GPU-Parallelization of Markov Chain Pool Decoding with Unbiased MCMC","publication_year":2026,"publication_date":"2026-08-31","ids":{"openalex":"https://openalex.org/W7204885596","doi":"https://doi.org/10.48550/arxiv.2608.30239"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.30239","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.30239","pdf_url":"https://arxiv.org/pdf/2608.30239","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.30239","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5159872877","display_name":"Takato Ueno","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I171494771","display_name":"Shiga University","ror":"https://ror.org/01vvhy971","country_code":"JP","type":"education","lineage":["https://openalex.org/I171494771"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ueno, Takato","raw_affiliation_strings":["Graduate School of Data Science , Shiga University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Data Science , Shiga University","institution_ids":["https://openalex.org/I171494771"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050611028","display_name":"Shuji Kijima","orcid":"https://orcid.org/0000-0001-6061-2330","observed_orcids":["https://orcid.org/0000-0001-6061-2330"]},"institutions":[{"id":"https://openalex.org/I171494771","display_name":"Shiga University","ror":"https://ror.org/01vvhy971","country_code":"JP","type":"education","lineage":["https://openalex.org/I171494771"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kijima, Shuji","raw_affiliation_strings":["Faculty of Data Science , Shiga University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Data Science , Shiga University","institution_ids":["https://openalex.org/I171494771"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I171494771"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9833999872207642,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9833999872207642,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.007699999958276749,"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/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gpu-parallelization","display_name":"GPU parallelization","score":0.9779999852180481},{"id":"https://openalex.org/keywords/clone-identification","display_name":"clone identification","score":0.4909999966621399},{"id":"https://openalex.org/keywords/group-testing","display_name":"group testing","score":0.7149999737739563},{"id":"https://openalex.org/keywords/stationary-distribution","display_name":"stationary distribution","score":0.871999979019165},{"id":"https://openalex.org/keywords/markov-chain-coupling","display_name":"Markov chain coupling","score":0.8970000147819519},{"id":"https://openalex.org/keywords/posterior-probability-estimation","display_name":"posterior probability estimation","score":0.6010000109672546}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.817799985408783},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.7663999795913696},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6406000256538391},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6204000115394592},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5774000287055969},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.4374000132083893},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4309000074863434},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4302999973297119},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.39640000462532043},{"id":"https://openalex.org/C98951983","wikidata":"https://www.wikidata.org/wiki/Q7604341","display_name":"Stationary distribution","level":3,"score":0.35830000042915344},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3352999985218048},{"id":"https://openalex.org/C199185054","wikidata":"https://www.wikidata.org/wiki/Q552299","display_name":"Chain (unit)","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.30329999327659607},{"id":"https://openalex.org/C97074811","wikidata":"https://www.wikidata.org/wiki/Q6771322","display_name":"Markov chain mixing time","level":5,"score":0.2937000095844269},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2782000005245209},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2770000100135803},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.25929999351501465},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.2590999901294708}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.30239","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.30239","pdf_url":"https://arxiv.org/pdf/2608.30239","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.30239","doi":"https://doi.org/10.48550/arxiv.2608.30239","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.30239","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,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.30239","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.30239","pdf_url":"https://arxiv.org/pdf/2608.30239","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204885596.pdf","grobid_xml":"https://content.openalex.org/works/W7204885596.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W4393924639","https://openalex.org/W7131393152"],"related_works":["https://openalex.org/W790035710","https://openalex.org/W2536111120","https://openalex.org/W2045491525","https://openalex.org/W2462471482","https://openalex.org/W1985802463","https://openalex.org/W2283727056","https://openalex.org/W2511891736","https://openalex.org/W3140817097","https://openalex.org/W2937912748","https://openalex.org/W1972416872"],"abstract_inverted_index":{"Markov":[0,76,119],"chain":[1,77],"pool":[2],"decoding":[3],"(MCPD)":[4],"devised":[5],"by":[6,110],"Knill":[7],"et":[8,98],"al.":[9,99],"(1996)":[10],"identifies":[11],"likely":[12],"positive":[13],"clones":[14,64],"from":[15],"noisy":[16],"pooled-test":[17],"results.":[18],"The":[19],"standard":[20,50],"MCPD":[21,44,51],"estimates":[22],"clone-wise":[23],"posterior":[24,82],"probabilities":[25],"using":[26],"Gibbs":[27],"sampling,":[28],"but":[29],"it":[30],"may":[31],"allocate":[32],"excessive":[33],"computational":[34],"effort":[35],"to":[36],"low-scoring":[37],"clones.":[38],"This":[39],"paper":[40],"focuses":[41],"on":[42,45,107],"parallelizing":[43],"GPU":[46,87],"architectures.":[47],"Whereas":[48],"the":[49,70,74,80,92,108,116],"employs":[52],"systematic-scan":[53],"updates,":[54],"we":[55,89],"propose":[56],"a":[57,103],"score-weighted":[58],"update":[59],"scheme":[60],"that":[61,69],"updates":[62],"high-scoring":[63],"more":[65],"frequently.":[66],"We":[67],"prove":[68],"stationary":[71],"distribution":[72],"of":[73,96,118],"proposed":[75],"coincides":[78],"with":[79,135],"target":[81],"distribution.":[83],"To":[84],"enable":[85],"efficient":[86],"parallelization,":[88],"further":[90],"incorporate":[91],"unbiased":[93],"MCMC":[94],"framework":[95],"Jacob":[97],"(2020)":[100],"and":[101,112,127],"employ":[102],"slot-refilling":[104],"technique":[105],"based":[106],"arguments":[109],"Glynn":[111],"Heidelberger":[113],"(1991)":[114],"about":[115],"coupling":[117],"chains.":[120],"Experiments":[121],"involving":[122],"1,298":[123],"clones,":[124],"97":[125],"pools,":[126],"three":[128],"true":[129],"positives":[130],"demonstrate":[131],"improved":[132],"recovery":[133],"compared":[134],"uniform":[136],"decoders,":[137],"while":[138],"maintaining":[139],"high":[140],"overlap":[141],"under":[142],"high-noise":[143],"conditions.":[144]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-02T00:00:00"}
