{"id":"https://openalex.org/W2783064067","doi":"https://doi.org/10.1109/bigdata.2017.8257929","title":"Performance characterization and acceleration of big data workloads on OpenPOWER system","display_name":"Performance characterization and acceleration of big data workloads on OpenPOWER system","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2783064067","doi":"https://doi.org/10.1109/bigdata.2017.8257929","mag":"2783064067"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8257929","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8257929","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","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/A5067748041","display_name":"Xiaoyi Lu","orcid":"https://orcid.org/0000-0001-7581-8905"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaoyi Lu","raw_affiliation_strings":["Department of Computer Science and Engineering, The Ohio State University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The Ohio State University","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016218994","display_name":"Haiyang Shi","orcid":"https://orcid.org/0000-0002-6105-4737"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haiyang Shi","raw_affiliation_strings":["Department of Computer Science and Engineering, The Ohio State University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The Ohio State University","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048025435","display_name":"Dipti Shankar","orcid":null},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dipti Shankar","raw_affiliation_strings":["Department of Computer Science and Engineering, The Ohio State University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The Ohio State University","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024879682","display_name":"Dhabaleswar K. Panda","orcid":"https://orcid.org/0000-0002-0356-1781"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dhabaleswar K. D K Panda","raw_affiliation_strings":["Department of Computer Science and Engineering, The Ohio State University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The Ohio State University","institution_ids":["https://openalex.org/I52357470"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067748041"],"corresponding_institution_ids":["https://openalex.org/I52357470"],"apc_list":null,"apc_paid":null,"fwci":1.4509,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.8787671,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"213","last_page":"222"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10101","display_name":"Cloud Computing and Resource Management","score":0.9998000264167786,"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.9998000264167786,"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/T10715","display_name":"Distributed and Parallel Computing Systems","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/remote-direct-memory-access","display_name":"Remote direct memory access","score":0.8789001107215881},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8291448354721069},{"id":"https://openalex.org/keywords/infiniband","display_name":"InfiniBand","score":0.7768108248710632},{"id":"https://openalex.org/keywords/ibm","display_name":"IBM","score":0.6950454115867615},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.6722913384437561},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.612381637096405},{"id":"https://openalex.org/keywords/spark","display_name":"SPARK (programming language)","score":0.575089156627655},{"id":"https://openalex.org/keywords/xeon","display_name":"Xeon","score":0.5055832862854004},{"id":"https://openalex.org/keywords/myrinet","display_name":"Myrinet","score":0.4783060848712921},{"id":"https://openalex.org/keywords/server","display_name":"Server","score":0.4434690773487091},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.24555009603500366},{"id":"https://openalex.org/keywords/message-passing","display_name":"Message passing","score":0.18884393572807312}],"concepts":[{"id":"https://openalex.org/C130795937","wikidata":"https://www.wikidata.org/wiki/Q2561570","display_name":"Remote direct memory access","level":2,"score":0.8789001107215881},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8291448354721069},{"id":"https://openalex.org/C2781030343","wikidata":"https://www.wikidata.org/wiki/Q922437","display_name":"InfiniBand","level":2,"score":0.7768108248710632},{"id":"https://openalex.org/C70388272","wikidata":"https://www.wikidata.org/wiki/Q5968558","display_name":"IBM","level":2,"score":0.6950454115867615},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.6722913384437561},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.612381637096405},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.575089156627655},{"id":"https://openalex.org/C145108525","wikidata":"https://www.wikidata.org/wiki/Q656154","display_name":"Xeon","level":2,"score":0.5055832862854004},{"id":"https://openalex.org/C2780601250","wikidata":"https://www.wikidata.org/wiki/Q1863181","display_name":"Myrinet","level":3,"score":0.4783060848712921},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.4434690773487091},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.24555009603500366},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.18884393572807312},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8257929","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8257929","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W312067013","https://openalex.org/W1502383304","https://openalex.org/W1532546444","https://openalex.org/W1963832798","https://openalex.org/W1968781019","https://openalex.org/W2059628440","https://openalex.org/W2071704971","https://openalex.org/W2082047517","https://openalex.org/W2112731002","https://openalex.org/W2120332949","https://openalex.org/W2140101134","https://openalex.org/W2285014189","https://openalex.org/W2415420807","https://openalex.org/W2487993327","https://openalex.org/W2511329048","https://openalex.org/W2528529254","https://openalex.org/W2529731748","https://openalex.org/W2538465729","https://openalex.org/W2580861338","https://openalex.org/W2584830274","https://openalex.org/W2621015395","https://openalex.org/W2637395137","https://openalex.org/W2758990545","https://openalex.org/W4235007850","https://openalex.org/W6610892132","https://openalex.org/W6631765917","https://openalex.org/W6739588732"],"related_works":["https://openalex.org/W2104094072","https://openalex.org/W1978254186","https://openalex.org/W4246679332","https://openalex.org/W2007129194","https://openalex.org/W2148030923","https://openalex.org/W2146390972","https://openalex.org/W3146239682","https://openalex.org/W1974037879","https://openalex.org/W1974732218","https://openalex.org/W2978025650"],"abstract_inverted_index":{"IBM's":[0],"POWER":[1,28,63],"processor":[2],"has":[3],"been":[4,76],"advocated":[5],"as":[6,88,138,211,240],"the":[7,17,20,40,46,66,79,120,125,141,180,243],"high-performance":[8],"architecture":[9,29],"designed":[10],"for":[11,27,81,184,207,235],"processing":[12,85,132],"Big":[13,34,130],"Data":[14,35,131],"workloads.":[15],"With":[16,188],"collaborations":[18],"through":[19],"OpenPOWER":[21,110,136,163,221,250],"Foundation,":[22],"more":[23,25],"and":[24,44,72,91,176,186,237],"innovations":[26],"are":[30,112],"emerging":[31],"to":[32,118,140,170,203,213,231,242],"solve":[33],"challenges.":[36],"For":[37],"example,":[38],"with":[39,217],"cooperation":[41],"between":[42],"IBM":[43],"Mellanox,":[45],"latest":[47],"generation":[48],"of":[49],"Remote":[50],"Direct":[51],"Memory":[52],"Access":[53],"(RDMA)":[54],"capable":[55],"InfiniBand":[56],"network":[57],"can":[58,124,200,228],"deliver":[59],"tremendous":[60],"performance":[61,101,155,193,205,233],"on":[62,98,157,220,248],"processors.":[64],"On":[65],"other":[67,106],"hand,":[68],"many":[69],"RDMA-based":[70,126,158,181],"designs":[71,169,199,246],"optimizations":[73],"recently":[74],"have":[75],"proposed":[77,198,226],"in":[78,179],"community":[80],"accelerating":[82],"big":[83],"data":[84],"systems":[86,111,137],"(such":[87],"Apache":[89],"Hadoop":[90,159,185,208,215,236],"Spark).":[92],"However,":[93],"these":[94,189],"studies":[95],"mostly":[96],"focus":[97],"achieving":[99],"higher":[100],"over":[102,135,162],"Intel":[103],"Xeon":[104],"or":[105],"x86":[107],"architectures.":[108],"As":[109],"getting":[113],"momentum,":[114],"we":[115],"set":[116],"out":[117],"answer":[119,146],"question":[121],"how":[122],"much":[123],"communication":[127,182],"runtime":[128],"benefit":[129],"middleware":[133],"running":[134,216,247],"compared":[139,212,241],"default":[142,214,244],"TCP/IP-based":[143],"designs.":[144],"To":[145],"this":[147,149],"question,":[148],"paper":[150],"first":[151],"presents":[152],"an":[153,249],"extensive":[154],"characterization":[156],"RPC":[160,209],"engine":[161,183],"system.":[164],"We":[165],"further":[166],"propose":[167],"new":[168],"enable":[171],"efficient":[172],"CPU":[173],"affinity":[174],"policies":[175],"architecture-aware":[177],"tuning":[178],"Spark.":[187],"various":[190],"accelerations,":[191],"our":[192,197,225],"evaluation":[194],"shows":[195],"that":[196],"achieve":[201],"up":[202,230],"2.73X":[204],"improvement":[206,234],"benchmark":[210],"IP-over-IB":[218],"protocol":[219],"systems.":[222],"In":[223],"addition,":[224],"design":[227],"gain":[229],"29.37%":[232],"Spark":[238],"workloads":[239],"RDMA":[245],"cluster.":[251]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
