{"id":"https://openalex.org/W4383987445","doi":"https://doi.org/10.48550/arxiv.2307.03930","title":"Rosko: Row Skipping Outer Products for Sparse Matrix Multiplication Kernels","display_name":"Rosko: Row Skipping Outer Products for Sparse Matrix Multiplication Kernels","publication_year":2023,"publication_date":"2023-07-08","ids":{"openalex":"https://openalex.org/W4383987445","doi":"https://doi.org/10.48550/arxiv.2307.03930"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2307.03930","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.03930","pdf_url":"https://arxiv.org/pdf/2307.03930","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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2307.03930","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062273176","display_name":"Vikas Natesh","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Natesh, Vikas","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018335085","display_name":"Andrew Sabot","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sabot, Andrew","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066867900","display_name":"H. T. Kung","orcid":"https://orcid.org/0000-0002-3348-3788"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kung, H. T.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5057509444","display_name":"Mark Ting","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ting, Mark","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5062273176"],"corresponding_institution_ids":[],"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9983000159263611,"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":0.9983000159263611,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9980000257492065,"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":0.9871000051498413,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7697271108627319},{"id":"https://openalex.org/keywords/matrix-multiplication","display_name":"Matrix multiplication","score":0.7665372490882874},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6902767419815063},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.6439087390899658},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6435143947601318},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.5159829258918762},{"id":"https://openalex.org/keywords/multiplication","display_name":"Multiplication (music)","score":0.5111454129219055},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.46991172432899475},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44131603837013245},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.43743598461151123},{"id":"https://openalex.org/keywords/vendor","display_name":"Vendor","score":0.42540639638900757},{"id":"https://openalex.org/keywords/computational-science","display_name":"Computational science","score":0.3515651822090149},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2626933455467224},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2014099657535553},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.10396227240562439},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09249424934387207}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7697271108627319},{"id":"https://openalex.org/C17349429","wikidata":"https://www.wikidata.org/wiki/Q1049914","display_name":"Matrix multiplication","level":3,"score":0.7665372490882874},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6902767419815063},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.6439087390899658},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6435143947601318},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.5159829258918762},{"id":"https://openalex.org/C2780595030","wikidata":"https://www.wikidata.org/wiki/Q3860309","display_name":"Multiplication (music)","level":2,"score":0.5111454129219055},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.46991172432899475},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44131603837013245},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.43743598461151123},{"id":"https://openalex.org/C2777338717","wikidata":"https://www.wikidata.org/wiki/Q1762621","display_name":"Vendor","level":2,"score":0.42540639638900757},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.3515651822090149},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2626933455467224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2014099657535553},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.10396227240562439},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09249424934387207},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.0},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2307.03930","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.03930","pdf_url":"https://arxiv.org/pdf/2307.03930","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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2307.03930","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2307.03930","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2307.03930","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.03930","pdf_url":"https://arxiv.org/pdf/2307.03930","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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4383987445.pdf","grobid_xml":"https://content.openalex.org/works/W4383987445.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3099313426","https://openalex.org/W4287593139","https://openalex.org/W752783541","https://openalex.org/W1506547947","https://openalex.org/W4206811032","https://openalex.org/W2995605830","https://openalex.org/W2086123442","https://openalex.org/W2596457687","https://openalex.org/W3212757063","https://openalex.org/W2093666864"],"abstract_inverted_index":{"We":[0,42],"propose":[1],"Rosko":[2,28,72,97,135],"--":[3,8],"row":[4,33,86],"skipping":[5,30,87],"outer":[6,78],"products":[7],"for":[9,66],"deriving":[10],"sparse":[11,45],"matrix":[12],"multiplication":[13],"(SpMM)":[14],"kernels":[15,47,98,136],"in":[16,132],"reducing":[17],"computation":[18],"and":[19,59,102,146],"memory":[20],"access":[21],"requirements":[22],"of":[23,31,117],"deep":[24],"neural":[25,118],"networks":[26],"(DNNs).":[27],"allows":[29],"entire":[32],"computations":[34],"during":[35],"program":[36],"execution":[37],"with":[38,76,123],"low":[39],"sparsity-management":[40],"overheads.":[41],"analytically":[43],"derive":[44],"CPU":[46],"that":[48],"adapt":[49],"to":[50,54,84,93,128,139],"given":[51],"hardware":[52,113],"characteristics":[53],"effectively":[55],"utilize":[56],"processor":[57],"cores":[58],"minimize":[60],"data":[61],"movement":[62],"without":[63],"the":[64],"need":[65],"auto-tuning":[67,101],"or":[68],"search":[69],"space":[70],"exploration.":[71],"can":[73],"be":[74],"integrated":[75],"other":[77],"product":[79],"scheduling":[80],"methods,":[81],"allowing":[82],"them":[83],"leverage":[85],"by":[88],"using":[89],"Rosko's":[90],"packing":[91],"format":[92],"skip":[94],"unnecessary":[95],"computation.":[96],"outperform":[99],"existing":[100],"search-based":[103],"solutions":[104],"as":[105,107],"well":[106],"state-of-the-art":[108],"vendor-optimized":[109],"libraries":[110],"on":[111,144],"real":[112],"across":[114],"a":[115,140],"variety":[116],"network":[119],"workloads.":[120],"For":[121],"matrices":[122],"sparsities":[124],"ranging":[125],"from":[126],"65%":[127],"99.8%":[129],"typically":[130],"found":[131],"machine":[133],"learning,":[134],"achieve":[137],"up":[138],"6.5x":[141],"runtime":[142],"reduction":[143],"Intel":[145],"ARM":[147],"CPUs.":[148]},"counts_by_year":[],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2023-07-12T00:00:00"}