{"id":"https://openalex.org/W7156793068","doi":"https://doi.org/10.48550/arxiv.2604.23466","title":"Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs","display_name":"Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs","publication_year":2026,"publication_date":"2026-04-25","ids":{"openalex":"https://openalex.org/W7156793068","doi":"https://doi.org/10.48550/arxiv.2604.23466"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.23466","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23466","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":"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.23466","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125587744","display_name":"Divakar Kumar Yadav","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yadav, Divakar Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134812704","display_name":"Tian Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134785973","display_name":"Deepak Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Deepak","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":0.7330999970436096,"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":0.7330999970436096,"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":0.09099999815225601,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.02370000071823597,"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/cuda","display_name":"CUDA","score":0.8475000262260437},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6159999966621399},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.5630999803543091},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4975999891757965},{"id":"https://openalex.org/keywords/general-purpose-computing-on-graphics-processing-units","display_name":"General-purpose computing on graphics processing units","score":0.4544999897480011},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.42179998755455017},{"id":"https://openalex.org/keywords/source-lines-of-code","display_name":"Source lines of code","score":0.42170000076293945},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.39419999718666077}],"concepts":[{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.8475000262260437},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.774399995803833},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6159999966621399},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.6075999736785889},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.5630999803543091},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4975999891757965},{"id":"https://openalex.org/C50630238","wikidata":"https://www.wikidata.org/wiki/Q971505","display_name":"General-purpose computing on graphics processing units","level":3,"score":0.4544999897480011},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.42179998755455017},{"id":"https://openalex.org/C199519371","wikidata":"https://www.wikidata.org/wiki/Q942695","display_name":"Source lines of code","level":3,"score":0.42170000076293945},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.39419999718666077},{"id":"https://openalex.org/C2779851693","wikidata":"https://www.wikidata.org/wiki/Q183484","display_name":"Graphics processing unit","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C21442007","wikidata":"https://www.wikidata.org/wiki/Q1027879","display_name":"Graphics","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C34165917","wikidata":"https://www.wikidata.org/wiki/Q188267","display_name":"Programming paradigm","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.34130001068115234},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.33230000734329224},{"id":"https://openalex.org/C78766204","wikidata":"https://www.wikidata.org/wiki/Q555032","display_name":"Multi-core processor","level":2,"score":0.33180001378059387},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.3287999927997589},{"id":"https://openalex.org/C2780728851","wikidata":"https://www.wikidata.org/wiki/Q468402","display_name":"Tile","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.2962000072002411},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.26930001378059387},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.263700008392334},{"id":"https://openalex.org/C139571649","wikidata":"https://www.wikidata.org/wiki/Q1156793","display_name":"Program optimization","level":3,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.23466","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23466","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.23466","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23466","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":"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"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.44550982117652893}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"NVIDIA's":[0],"CUDA":[1,153],"Tile":[2],"(CuTile)":[3],"introduces":[4],"a":[5,148],"Python-based,":[6],"tile-centric":[7],"abstraction":[8],"for":[9,113,144,151,158],"GPU":[10],"kernel":[11,127,166],"development":[12],"that":[13,95],"aims":[14],"to":[15,86,110],"simplify":[16],"programming":[17],"while":[18,120],"retaining":[19],"Tensor":[20,23],"Core":[21],"and":[22,48,57,62,79,90,101],"Memory":[24],"Accelerator":[25],"(TMA)":[26],"efficiency":[27],"on":[28,51,173],"modern":[29],"GPUs.":[30],"We":[31,69],"present":[32],"the":[33,162],"first":[34],"independent,":[35],"cross-architecture":[36,180],"evaluation":[37],"of":[38,125,134,140,170,188],"CuTile":[39,96,107,131,164],"against":[40],"established":[41],"approaches":[42],"such":[43],"as":[44],"cuBLAS,":[45],"Triton,":[46],"WMMA,":[47],"raw":[49],"SIMT":[50],"three":[52],"NVIDIA":[53],"GPUs":[54],"spanning":[55],"Hopper":[56],"Blackwell:":[58],"H100":[59],"NVL,":[60],"B200,":[61],"RTX":[63,174],"PRO":[64,175],"6000":[65,176],"Blackwell":[66,105],"Server":[67],"Edition.":[68],"benchmark":[70],"representative":[71],"AI":[72],"workloads,":[73],"including":[74],"GEMM,":[75,130],"fused":[76,114],"multi-head":[77],"attention,":[78,115],"end-to-end":[80],"LLM":[81],"inference":[82],"in":[83,137],"BF16/FP16":[84],"precision,":[85],"assess":[87],"both":[88],"performance":[89,136,190],"portability.":[91,201],"Our":[92],"results":[93],"show":[94],"effectiveness":[97],"is":[98],"strongly":[99],"workload-":[100],"architecture-dependent.":[102],"On":[103],"datacenter-class":[104],"(B200),":[106],"achieves":[108,167],"up":[109],"1007":[111],"TFLOP/s":[112],"outperforming":[116],"FlashAttention-2":[117,171],"by":[118],"2.5x":[119],"requiring":[121],"only":[122,168],"60":[123],"lines":[124,139],"Python":[126],"code.":[128],"For":[129],"reaches":[132],"52-79%":[133],"cuBLAS":[135,189],"22":[138],"code":[141],"(versus":[142],"123":[143],"WMMA),":[145],"making":[146],"it":[147],"practical":[149],"replacement":[150],"hand-written":[152],"kernels":[154],"but":[155],"not":[156],"yet":[157],"vendor-optimized":[159],"libraries.":[160],"However,":[161],"same":[163],"attention":[165],"53%":[169],"throughput":[172],"(sm_120),":[177],"exposing":[178],"significant":[179],"optimization":[181],"gaps.":[182],"In":[183],"contrast,":[184],"Triton":[185],"sustains":[186],"62-101%":[187],"across":[191],"all":[192],"tested":[193],"platforms":[194],"without":[195],"architecture-specific":[196],"tuning,":[197],"demonstrating":[198],"substantially":[199],"stronger":[200]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-29T00:00:00"}
