{"id":"https://openalex.org/W7138844257","doi":"https://doi.org/10.48550/arxiv.2603.16164","title":"AI Application Benchmarking: Power-Aware Performance Analysis for Vision and Language Models","display_name":"AI Application Benchmarking: Power-Aware Performance Analysis for Vision and Language Models","publication_year":2026,"publication_date":"2026-03-17","ids":{"openalex":"https://openalex.org/W7138844257","doi":"https://doi.org/10.48550/arxiv.2603.16164"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.16164","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16164","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.2603.16164","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130128604","display_name":"Martin Mayr","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mayr, Martin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117210978","display_name":"Sebastian Wind","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wind, Sebastian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110711162","display_name":"Lukas Schr\u00f6der","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schr\u00f6der, Lukas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129902839","display_name":"Georg Hager","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moradi, Mohammadmoein","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130102328","display_name":"Harald K\u00f6stler","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hager, Georg","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130125380","display_name":"Gerhard Wellein","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"K\u00f6stler, Harald","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Wellein, Gerhard","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wellein, Gerhard","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/T14347","display_name":"Big Data and Digital Economy","score":0.2924000024795532,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T14347","display_name":"Big Data and Digital Economy","score":0.2924000024795532,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.22669999301433563,"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.11500000208616257,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/benchmarking","display_name":"Benchmarking","score":0.8944000005722046},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.6759999990463257},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5993000268936157},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5590999722480774},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4733000099658966},{"id":"https://openalex.org/keywords/performance-metric","display_name":"Performance metric","score":0.41449999809265137},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4018000066280365}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.8944000005722046},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8050000071525574},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.6759999990463257},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5993000268936157},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5590999722480774},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5149000287055969},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4733000099658966},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4383000135421753},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.41449999809265137},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4018000066280365},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3587999939918518},{"id":"https://openalex.org/C118993495","wikidata":"https://www.wikidata.org/wiki/Q5042828","display_name":"Electrical efficiency","level":3,"score":0.33660000562667847},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.30079999566078186},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.2680000066757202},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.26579999923706055},{"id":"https://openalex.org/C157170001","wikidata":"https://www.wikidata.org/wiki/Q4781507","display_name":"Applications of artificial intelligence","level":2,"score":0.25940001010894775}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.16164","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16164","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.2603.16164","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16164","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":[{"id":"https://metadata.un.org/sdg/7","score":0.4262538254261017,"display_name":"Affordable and clean energy"},{"id":"https://metadata.un.org/sdg/9","score":0.41107437014579773,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Artificial":[0],"Intelligence":[1],"(AI)":[2],"workloads":[3,28],"drive":[4],"a":[5,21,56],"rapid":[6],"expansion":[7],"of":[8,35],"high-performance":[9],"computing":[10],"(HPC)":[11],"infrastructures":[12,43],"and":[13,17,29,44,69,71,77,103,115,137,165],"increase":[14],"their":[15,30,149],"power":[16,52,126],"energy":[18,47,104],"demands":[19],"towards":[20],"critical":[22,39],"level.":[23],"AI":[24],"benchmarks":[25],"representing":[26],"state-of-the-art":[27],"understanding":[31],"in":[32,98,148],"the":[33,95,130,141],"context":[34],"performance-energy":[36,157],"trade-offs":[37],"are":[38],"to":[40,91],"deploy":[41],"efficient":[42],"can":[45],"guide":[46],"efficiency":[48,105,131],"measures,":[49],"such":[50],"as":[51,129],"limiting.":[53],"We":[54,100],"introduce":[55],"benchmarking":[57],"framework":[58],"with":[59],"popular":[60],"deep":[61],"learning":[62],"applications":[63],"from":[64],"computer":[65],"vision":[66],"(image":[67],"classification":[68],"generation)":[70],"large":[72],"language":[73],"models":[74],"(continued":[75],"pre-training":[76],"inference)":[78],"implementing":[79],"modern":[80],"methods.":[81],"Our":[82,119],"performance":[83,102],"analysis":[84],"focuses":[85],"on":[86,110,162],"throughput":[87],"rather":[88],"than":[89],"``time":[90],"completion'',":[92],"which":[93,145],"is":[94,160],"standard":[96],"metric":[97],"HPC.":[99],"analyse":[101],"under":[106],"various":[107],"power-limit":[108],"settings":[109],"NVIDIA":[111,113,143],"H100,":[112],"H200,":[114],"AMD":[116],"MI300X":[117],"GPUs.":[118],"results":[120],"reveal":[121],"that":[122],"no":[123],"universal":[124],"optimal":[125],"limit":[127],"exists,":[128],"peak":[132],"varies":[133],"across":[134],"application":[135],"types":[136],"GPU":[138],"architectures.":[139],"Interestingly,":[140],"two":[142],"GPUs":[144],"mainly":[146],"differ":[147],"high-bandwidth":[150],"memory":[151],"(HBM)":[152],"configuration":[153],"show":[154],"qualitatively":[155],"different":[156],"trade-offs.":[158],"Code":[159],"available":[161],"Zenodo":[163],"(https://zenodo.org/records/20083679)":[164],"GitHub":[166],"(https://github.com/RRZE-HPC/hpc-ai-perf-bench).":[167]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-20T00:00:00"}
