{"id":"https://openalex.org/W7148590007","doi":"https://doi.org/10.48550/arxiv.2604.00736","title":"Is RISC-V Ready for Machine Learning? Portable Gaussian Processes Using Asynchronous Tasks","display_name":"Is RISC-V Ready for Machine Learning? Portable Gaussian Processes Using Asynchronous Tasks","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148590007","doi":"https://doi.org/10.48550/arxiv.2604.00736"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00736","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00736","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.2604.00736","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091937184","display_name":"Alexander Strack","orcid":"https://orcid.org/0000-0002-9939-9044"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Strack, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132780318","display_name":"Patrick Diehl","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Diehl, Patrick","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132795938","display_name":"Dirk Pfl\u00fcger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pfl\u00fcger, Dirk","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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.3458999991416931,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.3458999991416931,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.22540000081062317,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.05119999870657921,"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/scalability","display_name":"Scalability","score":0.6629999876022339},{"id":"https://openalex.org/keywords/software-portability","display_name":"Software portability","score":0.6363000273704529},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.6132000088691711},{"id":"https://openalex.org/keywords/factor","display_name":"Factor (programming language)","score":0.5684999823570251},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4821999967098236},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4472000002861023},{"id":"https://openalex.org/keywords/performance-tuning","display_name":"Performance tuning","score":0.42669999599456787},{"id":"https://openalex.org/keywords/performance-improvement","display_name":"Performance improvement","score":0.4115999937057495},{"id":"https://openalex.org/keywords/multithreading","display_name":"Multithreading","score":0.3880999982357025},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.38029998540878296}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.829800009727478},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6629999876022339},{"id":"https://openalex.org/C63000827","wikidata":"https://www.wikidata.org/wiki/Q3080428","display_name":"Software portability","level":2,"score":0.6363000273704529},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.6132000088691711},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.5684999823570251},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.49230000376701355},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.484499990940094},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4821999967098236},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4472000002861023},{"id":"https://openalex.org/C2777138346","wikidata":"https://www.wikidata.org/wiki/Q1714153","display_name":"Performance tuning","level":2,"score":0.42669999599456787},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.4115999937057495},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.38839998841285706},{"id":"https://openalex.org/C201410400","wikidata":"https://www.wikidata.org/wiki/Q1064412","display_name":"Multithreading","level":3,"score":0.3880999982357025},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.38029998540878296},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C41681595","wikidata":"https://www.wikidata.org/wiki/Q7917855","display_name":"Vectorization (mathematics)","level":2,"score":0.3718999922275543},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3517000079154968},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C2778514511","wikidata":"https://www.wikidata.org/wiki/Q1374194","display_name":"Programmer","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C83283714","wikidata":"https://www.wikidata.org/wiki/Q121117","display_name":"Supercomputer","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.30000001192092896},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C169590947","wikidata":"https://www.wikidata.org/wiki/Q47506","display_name":"Compiler","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C86111242","wikidata":"https://www.wikidata.org/wiki/Q859595","display_name":"Coprocessor","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C185410017","wikidata":"https://www.wikidata.org/wiki/Q7171778","display_name":"Petascale computing","level":3,"score":0.26750001311302185},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C52723943","wikidata":"https://www.wikidata.org/wiki/Q1127410","display_name":"Serialization","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C118021083","wikidata":"https://www.wikidata.org/wiki/Q610398","display_name":"System on a chip","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2542000114917755},{"id":"https://openalex.org/C2777655017","wikidata":"https://www.wikidata.org/wiki/Q1501161","display_name":"Toolbox","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00736","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00736","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.2604.00736","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00736","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":[{"display_name":"Industry, innovation and infrastructure","score":0.470033198595047,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Gaussian":[0,69],"processes":[1],"are":[2],"widely":[3],"used":[4],"in":[5],"machine":[6],"learning":[7],"domains":[8],"but":[9],"remain":[10],"computationally":[11],"demanding,":[12],"limiting":[13],"their":[14],"efficient":[15],"scalability":[16],"across":[17,43],"emerging":[18],"hardware":[19,45],"platforms.":[20],"The":[21,125],"GPRat":[22,39],"library":[23],"addresses":[24],"these":[25],"challenges":[26],"using":[27],"the":[28,89,101,109,115,178,188,197],"HPX":[29],"asynchronous":[30],"many-task":[31,208],"runtime":[32],"system.":[33],"In":[34],"this":[35],"work,":[36],"we":[37],"extend":[38],"to":[40,75,113,144,157,196],"enable":[41],"portability":[42],"multiple":[44],"architectures":[46],"and":[47,55,63,72,81,135,149,167,194],"evaluate":[48],"its":[49],"performance":[50,98,134,140,172],"on":[51],"representative":[52],"x86-64,":[53],"ARM,":[54],"RISC-V":[56,129,202],"chips.":[57,184],"We":[58],"conduct":[59],"node-level":[60],"strong":[61],"scaling":[62,66,107],"problem":[64,163],"size":[65,164],"benchmarks":[67],"for":[68,200],"process":[70],"prediction":[71],"hyperparameter":[73],"optimization":[74],"assess":[76],"single-core":[77,97,139],"performance,":[78],"parallel":[79,106,151],"scalability,":[80,137],"architectural":[82],"efficiency.":[83],"Our":[84],"results":[85],"show":[86],"that":[87],"while":[88],"x86-64":[90,168],"Zen":[91,117],"2":[92,118],"chip":[93,112,130],"achieves":[94],"a":[95,145,158],"58%":[96],"advantage":[99],"over":[100],"ARM-based":[102],"Fujitsu":[103],"A64FX,":[104],"superior":[105],"allows":[108],"48-core":[110],"ARM":[111,166,183],"outperform":[114],"64-core":[116],"by":[119,142,207],"9%":[120],"at":[121],"full":[122],"node":[123],"utilization.":[124],"evaluated":[126],"SOPHON":[127],"SG2042":[128],"exhibits":[131],"substantially":[132],"lower":[133],"weaker":[136],"with":[138],"lagging":[141],"up":[143,156],"factor":[146,159],"of":[147,155,160,181,190],"14":[148],"large-scale":[150],"workloads":[152],"showing":[153],"slowdowns":[154],"24.":[161],"For":[162],"scaling,":[165],"systems":[169],"demonstrate":[170],"comparable":[171],"within":[173],"23%.":[174],"These":[175],"findings":[176],"highlight":[177],"growing":[179],"competitiveness":[180],"purpose-built":[182],"Furthermore,":[185],"they":[186],"underscore":[187],"importance":[189],"wide-register":[191],"vectorization":[192],"support":[193],"improvements":[195],"memory":[198],"subsystem":[199],"upcoming":[201],"platforms,":[203],"especially":[204],"when":[205],"targeted":[206],"runtimes.":[209]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-03T00:00:00"}