{"id":"https://openalex.org/W3115858121","doi":"https://doi.org/10.1109/hpec43674.2020.9286224","title":"Using RAPIDS AI to Accelerate Graph Data Science Workflows","display_name":"Using RAPIDS AI to Accelerate Graph Data Science Workflows","publication_year":2020,"publication_date":"2020-09-22","ids":{"openalex":"https://openalex.org/W3115858121","doi":"https://doi.org/10.1109/hpec43674.2020.9286224","mag":"3115858121"},"language":"en","primary_location":{"id":"doi:10.1109/hpec43674.2020.9286224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec43674.2020.9286224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080548667","display_name":"Todd Hricik","orcid":null},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Todd Hricik","raw_affiliation_strings":["Ying Wu Colloge of Computing, New Jersey Institutue of Technology, Newark, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Ying Wu Colloge of Computing, New Jersey Institutue of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076610730","display_name":"David A. Bader","orcid":"https://orcid.org/0000-0002-7380-5876"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Bader","raw_affiliation_strings":["Ying Wu Colloge of Computing, New Jersey Institutue of Technology, Newark, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Ying Wu Colloge of Computing, New Jersey Institutue of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086521826","display_name":"Oded Green","orcid":"https://orcid.org/0000-0003-3658-1233"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Oded Green","raw_affiliation_strings":["NVIDIA Corporation, Santa Clara, CA, USA"],"affiliations":[{"raw_affiliation_string":"NVIDIA Corporation, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I4210127875"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5080548667"],"corresponding_institution_ids":["https://openalex.org/I118118575"],"apc_list":null,"apc_paid":null,"fwci":1.3277,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.80748745,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9937999844551086,"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/T13283","display_name":"Mental Health Research Topics","score":0.9825999736785889,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8495931625366211},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.6820440888404846},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.6371097564697266},{"id":"https://openalex.org/keywords/porting","display_name":"Porting","score":0.634253740310669},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6250448226928711},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5510573387145996},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4307671785354614},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3724448084831238},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.33783602714538574},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.33418381214141846},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3323150873184204},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.2413632869720459},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.20955529808998108}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8495931625366211},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.6820440888404846},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.6371097564697266},{"id":"https://openalex.org/C106251023","wikidata":"https://www.wikidata.org/wiki/Q851989","display_name":"Porting","level":3,"score":0.634253740310669},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6250448226928711},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5510573387145996},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4307671785354614},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3724448084831238},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33783602714538574},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.33418381214141846},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3323150873184204},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.2413632869720459},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.20955529808998108}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hpec43674.2020.9286224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec43674.2020.9286224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1643412971","https://openalex.org/W1976969221","https://openalex.org/W1994687138","https://openalex.org/W2000042664","https://openalex.org/W2020867567","https://openalex.org/W2027963417","https://openalex.org/W2056720759","https://openalex.org/W2059930487","https://openalex.org/W2115682136","https://openalex.org/W2140849606","https://openalex.org/W2796901959","https://openalex.org/W2962849769","https://openalex.org/W2997134027","https://openalex.org/W3101452997","https://openalex.org/W3103362336","https://openalex.org/W6657528111","https://openalex.org/W6677479164"],"related_works":["https://openalex.org/W2356602486","https://openalex.org/W2351992668","https://openalex.org/W2324828474","https://openalex.org/W2374315191","https://openalex.org/W2391207559","https://openalex.org/W2384715785","https://openalex.org/W2349624418","https://openalex.org/W2064459023","https://openalex.org/W2384611437","https://openalex.org/W2347873412"],"abstract_inverted_index":{"Scale":[0],"free":[1,48],"networks":[2],"are":[3],"abundant":[4],"in":[5,51,228,262],"many":[6,24],"natural,":[7],"social,":[8],"and":[9,70,105,161,168,188,209,265,269],"engineering":[10],"phenomena":[11],"for":[12,42,89,101,119,129,195],"which":[13],"there":[14],"exists":[15],"a":[16,52,87,109,115,121,146,154,203,212,250],"substantial":[17],"corpus":[18],"of":[19,25,36,46,64,84,145,156,198,206,221,248,259],"theory":[20],"able":[21],"to":[22,72,97,123,138,193,215,233,244,272],"elucidate":[23],"their":[26],"underlying":[27],"properties.":[28],"In":[29],"this":[30],"paper":[31],"we":[32,92,113],"study":[33],"the":[34,43,76,94,99,103,165,177,190,196,207,216,246,257,263,267,273],"scalability":[35],"some":[37],"widely":[38,157],"available":[39],"Python-based":[40],"tools":[41],"empirical":[44,124,217],"investigation":[45],"scale":[47,130,268],"network":[49,150],"data":[50,68,159,185,208],"typical":[53,147],"early":[54,148],"stage":[55,149],"analysis":[56,100,151],"pipeline.":[57],"We":[58,171],"demonstrate":[59,237],"how":[60],"porting":[61],"serial":[62,104,143],"implementations":[63],"commonly":[65],"used":[66,158],"pipeline":[67,91],"structures":[69,160],"methods":[71,137],"parallel":[73],"hardware":[74],"via":[75],"NVIDIA":[77],"RAPIDS":[78,182],"AI":[79,183],"API":[80],"requires":[81],"minimal":[82],"rewriting":[83],"code.":[85],"As":[86],"utility":[88],"each":[90],"recorded":[93],"time":[95,192,241],"required":[96,243],"complete":[98,245],"both":[102],"parallelized":[106,173],"workflows":[107],"on":[108,225],"task-wise":[110],"basis.":[111],"Furthermore,":[112],"review":[114],"statistically":[116],"based":[117,136],"methodology":[118],"fitting":[120,211,266],"power-law":[122,213],"data.":[125,275],"Maximum":[126],"likelihood":[127],"estimations":[128],"were":[131],"inferred":[132],"after":[133],"using":[134,176],"Kolmogorov-Smirnov":[135],"determine":[139],"location":[140,270],"estimates.":[141],"Our":[142],"implementation":[144],"workflow":[152,175],"uses":[153],"combination":[155],"algorithms":[162],"provided":[163,179],"by":[164,180],"NumPy,":[166],"Pandas":[167],"NetworkX":[169],"frameworks.":[170],"then":[172],"our":[174,222],"APIs":[178],"NVIDIA's":[181],"open":[184],"science":[186],"libraries":[187],"measured":[189],"relative":[191],"completion":[194],"tasks":[197,247],"ingesting":[199],"raw":[200],"data,":[201],"creating":[202],"graph":[204,251,264],"representation":[205],"finally":[210],"distribution":[214],"observations.":[218],"The":[219],"results":[220],"experiments,":[223],"run":[224],"graphs":[226],"ranging":[227],"size":[229],"from":[230,252],"1":[231],"million":[232,235],"20":[234],"edges,":[236],"that":[238],"significantly":[239],"less":[240],"is":[242],"generating":[249],"an":[253],"edge":[254],"list,":[255],"computing":[256],"degree":[258],"all":[260],"nodes":[261],"parameters":[271],"observed":[274]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}