{"id":"https://openalex.org/W3011270632","doi":"https://doi.org/10.1109/iiswc47752.2019.9042114","title":"Efficacy of Statistical Sampling on Contemporary Workloads: The Case of SPEC CPU2017","display_name":"Efficacy of Statistical Sampling on Contemporary Workloads: The Case of SPEC CPU2017","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3011270632","doi":"https://doi.org/10.1109/iiswc47752.2019.9042114","mag":"3011270632"},"language":"en","primary_location":{"id":"doi:10.1109/iiswc47752.2019.9042114","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iiswc47752.2019.9042114","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Workload Characterization (IISWC)","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/A5063739412","display_name":"Sarabjeet Singh","orcid":"https://orcid.org/0000-0003-3032-1916"},"institutions":[{"id":"https://openalex.org/I347237974","display_name":"Ashoka University","ror":"https://ror.org/02j1xr113","country_code":"IN","type":"education","lineage":["https://openalex.org/I347237974"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sarabjeet Singh","raw_affiliation_strings":["Ashoka University"],"affiliations":[{"raw_affiliation_string":"Ashoka University","institution_ids":["https://openalex.org/I347237974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012432339","display_name":"Manu Awasthi","orcid":"https://orcid.org/0000-0002-5616-9679"},"institutions":[{"id":"https://openalex.org/I347237974","display_name":"Ashoka University","ror":"https://ror.org/02j1xr113","country_code":"IN","type":"education","lineage":["https://openalex.org/I347237974"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manu Awasthi","raw_affiliation_strings":["Ashoka University"],"affiliations":[{"raw_affiliation_string":"Ashoka University","institution_ids":["https://openalex.org/I347237974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5063739412"],"corresponding_institution_ids":["https://openalex.org/I347237974"],"apc_list":null,"apc_paid":null,"fwci":0.4815,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.6447952,"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":"70","last_page":"80"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":1.0,"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":1.0,"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/T11181","display_name":"Advanced Data Storage Technologies","score":0.9995999932289124,"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.9972000122070312,"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/spec#","display_name":"Spec#","score":0.8268439769744873},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.8155996799468994},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8057405948638916},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.743563175201416},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5622520446777344},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5512592792510986},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.5440146923065186},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5359267592430115},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.5303996801376343},{"id":"https://openalex.org/keywords/percentile","display_name":"Percentile","score":0.43956267833709717},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.42142534255981445},{"id":"https://openalex.org/keywords/parsec","display_name":"Parsec","score":0.4108857810497284},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.34301328659057617},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2102372646331787},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.16919824481010437},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.133419930934906},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11706897616386414}],"concepts":[{"id":"https://openalex.org/C2778565505","wikidata":"https://www.wikidata.org/wiki/Q2207566","display_name":"Spec#","level":2,"score":0.8268439769744873},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8155996799468994},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8057405948638916},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.743563175201416},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5622520446777344},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5512592792510986},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.5440146923065186},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5359267592430115},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.5303996801376343},{"id":"https://openalex.org/C122048520","wikidata":"https://www.wikidata.org/wiki/Q2913954","display_name":"Percentile","level":2,"score":0.43956267833709717},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.42142534255981445},{"id":"https://openalex.org/C44060867","wikidata":"https://www.wikidata.org/wiki/Q12129","display_name":"Parsec","level":3,"score":0.4108857810497284},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.34301328659057617},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2102372646331787},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.16919824481010437},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.133419930934906},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11706897616386414},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C150846664","wikidata":"https://www.wikidata.org/wiki/Q7602306","display_name":"Stars","level":2,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iiswc47752.2019.9042114","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iiswc47752.2019.9042114","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Workload Characterization (IISWC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W49459401","https://openalex.org/W1935748272","https://openalex.org/W1977556410","https://openalex.org/W2012431717","https://openalex.org/W2015691124","https://openalex.org/W2034062945","https://openalex.org/W2036853599","https://openalex.org/W2078455576","https://openalex.org/W2095751447","https://openalex.org/W2096864363","https://openalex.org/W2098005539","https://openalex.org/W2107493680","https://openalex.org/W2113235308","https://openalex.org/W2118811116","https://openalex.org/W2122774514","https://openalex.org/W2134633067","https://openalex.org/W2150196852","https://openalex.org/W2153456949","https://openalex.org/W2163567002","https://openalex.org/W2164821985","https://openalex.org/W2299026636","https://openalex.org/W2537522345","https://openalex.org/W2574836134","https://openalex.org/W2794945502","https://openalex.org/W2796645376","https://openalex.org/W2805359884","https://openalex.org/W2806944479","https://openalex.org/W2932835273","https://openalex.org/W2963679008","https://openalex.org/W3006335209","https://openalex.org/W3098304703","https://openalex.org/W4239813889","https://openalex.org/W4245923077","https://openalex.org/W4253029824","https://openalex.org/W4253086812","https://openalex.org/W6602011513","https://openalex.org/W6644682428","https://openalex.org/W6677677767","https://openalex.org/W6697834529"],"related_works":["https://openalex.org/W878150521","https://openalex.org/W2008941207","https://openalex.org/W3082894236","https://openalex.org/W2169875292","https://openalex.org/W1838930658","https://openalex.org/W3213800328","https://openalex.org/W1986004968","https://openalex.org/W2805359884","https://openalex.org/W2186315912","https://openalex.org/W4252566889"],"abstract_inverted_index":{"New":[0],"benchmark":[1],"suites":[2,65],"are":[3,25,83],"constantly":[4],"being":[5,58],"released,":[6],"with":[7,50,228,272],"each":[8],"one":[9],"providing":[10],"a":[11,51,80,105,208],"much":[12],"larger":[13],"set":[14],"of":[15,21,44,53,108,132,148,207,239],"benchmarks,":[16,100],"representing":[17,55],"an":[18,114,146,236],"ever-growing":[19],"variety":[20,52],"workloads.":[22],"Contemporary":[23],"workloads":[24,54],"increasingly":[26,85],"more":[27],"complex":[28,63],"in":[29,99,171,275,290],"their":[30],"computational":[31],"and":[32,62,101,120,126,142,178,234,271],"memory":[33,262,291],"footprints.":[34],"Most":[35],"computer":[36],"architecture":[37],"research":[38],"is":[39,145],"based":[40],"on":[41,87,193,231,252],"the":[42,56,130,136,151,164,172,179,201,215,240,250],"ability":[43],"researchers":[45],"to":[46,71,77,104,221,277,296],"simulate":[47,72],"novel":[48],"ideas":[49],"domain":[57],"researched.":[59],"However,":[60],"bigger":[61],"benchmarks":[64,74],"have":[66],"made":[67],"it":[68],"extremely":[69],"impractical":[70],"complete":[73],"from":[75,235],"start":[76],"finish.":[78],"As":[79],"result,":[81],"architects":[82],"becoming":[84],"dependent":[86],"statistical":[88],"sampling":[89],"techniques":[90,112],"like":[91,261],"SimPoints,":[92,182,244],"which":[93,144,211],"identify":[94],"long,":[95],"repetitive":[96],"execution":[97],"phases":[98,197],"limit":[102],"simulations":[103],"few":[106],"instances":[107],"these":[109],"phases.":[110],"These":[111],"present":[113,259],"inherent":[115],"trade-off":[116],"between":[117,175,249],"simulation":[118,133,160,185,217],"speed":[119],"accuracy.":[121],"This":[122],"work":[123],"presents":[124],"results":[125],"insights":[127],"for":[128,135,150],"determining":[129],"accuracy":[131],"points":[134,161],"SPEC":[137],"CPU2017":[138],"suite,":[139],"using":[140,181,225,243],"Pin":[141],"PinPoints,":[143],"implementation":[147],"SimPoints":[149,266],"\u00d786":[152],"ISA.":[153],"Our":[154],"analysis":[155],"concludes":[156],"that":[157,192],"carefully":[158],"chosen":[159,283],"faithfully":[162,199],"represent":[163,200],"workload;":[165],"we":[166,245,258],"observe":[167],"<;":[168],"1%":[169],"variance":[170],"instruction":[173],"distribution":[174],"full":[176,297],"runs":[177],"ones":[180],"while":[183],"reducing":[184],"time":[186,218],"by":[187,219,301],"~750\u00d7.":[188],"We":[189],"also":[190],"show":[191,287],"average,":[194],"just":[195],"12":[196],"can":[198,212,286],"90":[202],"<sup":[203],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[204],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">th</sup>":[205],"percentile":[206],"benchmark's":[209],"behavior,":[210],"help":[213],"reduce":[214],"overall":[216],"up":[220],"~1297\u00d7.":[222],"In":[223],"addition,":[224],"performance":[226],"statistics":[227],"native":[229],"binaries":[230],"real":[232],"hardware":[233],"architectural":[237],"model":[238],"same":[241],"machine":[242],"report":[246],"good":[247],"co-relations":[248],"two":[251],"metrics":[253],"such":[254],"as":[255,294,299],"CPI.":[256],"Finally,":[257],"cases":[260],"hierarchy":[263,292],"explorations,":[264],"where":[265],"should":[267],"be":[268],"used":[269],"judiciously":[270],"extreme":[273],"caution":[274],"order":[276],"derive":[278],"correct":[279],"conclusions":[280],"-":[281],"inappropriately":[282],"SimPoint":[284],"configurations":[285],"large":[288],"deviations":[289],"behavior":[293],"compared":[295],"runs,":[298],"reported":[300],"prior":[302],"studies.":[303]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
