{"id":"https://openalex.org/W4414898619","doi":"https://doi.org/10.1109/cluster59342.2025.11186479","title":"Detecting Silent Data Corruption from Hardware Counters","display_name":"Detecting Silent Data Corruption from Hardware Counters","publication_year":2025,"publication_date":"2025-09-02","ids":{"openalex":"https://openalex.org/W4414898619","doi":"https://doi.org/10.1109/cluster59342.2025.11186479"},"language":"en","primary_location":{"id":"doi:10.1109/cluster59342.2025.11186479","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cluster59342.2025.11186479","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Cluster Computing (CLUSTER)","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/A5029735974","display_name":"Minseop Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minseop Choi","raw_affiliation_strings":["UMass Lowell,Dept. of Electrical &#x0026; Computer Eng.,Lowell,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UMass Lowell,Dept. of Electrical &#x0026; Computer Eng.,Lowell,USA","institution_ids":["https://openalex.org/I133738476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009774499","display_name":"Taha Azzaoui","orcid":null},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Taha Azzaoui","raw_affiliation_strings":["UMass Lowell,Dept. of Electrical &#x0026; Computer Eng.,Lowell,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UMass Lowell,Dept. of Electrical &#x0026; Computer Eng.,Lowell,USA","institution_ids":["https://openalex.org/I133738476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119879251","display_name":"Kyle Chaisson","orcid":null},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kyle Chaisson","raw_affiliation_strings":["UMass Lowell,Dept. of Electrical &#x0026; Computer Eng.,Lowell,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UMass Lowell,Dept. of Electrical &#x0026; Computer Eng.,Lowell,USA","institution_ids":["https://openalex.org/I133738476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014413722","display_name":"Orlando Arias","orcid":"https://orcid.org/0009-0002-3948-5773"},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Orlando Arias","raw_affiliation_strings":["UMass Lowell,Dept. of Electrical &#x0026; Computer Eng.,Lowell,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UMass Lowell,Dept. of Electrical &#x0026; Computer Eng.,Lowell,USA","institution_ids":["https://openalex.org/I133738476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102805559","display_name":"Seung Woo Son","orcid":"https://orcid.org/0000-0001-8922-418X"},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Seung Woo Son","raw_affiliation_strings":["UMass Lowell,Dept. of Electrical &#x0026; Computer Eng.,Lowell,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UMass Lowell,Dept. of Electrical &#x0026; Computer Eng.,Lowell,USA","institution_ids":["https://openalex.org/I133738476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I133738476"],"apc_list":null,"apc_paid":null,"fwci":1.6508,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.88330973,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11424","display_name":"Security and Verification in Computing","score":0.995199978351593,"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"}},"topics":[{"id":"https://openalex.org/T11424","display_name":"Security and Verification in Computing","score":0.995199978351593,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.9761000275611877,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.7365999817848206},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5827000141143799},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5504999756813049},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5444999933242798},{"id":"https://openalex.org/keywords/error-detection-and-correction","display_name":"Error detection and correction","score":0.4724000096321106},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.3955000042915344},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.34049999713897705},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.3068000078201294}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7724999785423279},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.7365999817848206},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5827000141143799},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5504999756813049},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5444999933242798},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.4724000096321106},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.43479999899864197},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.428600013256073},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.39910000562667847},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3955000042915344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3560999929904938},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.34049999713897705},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3172999918460846},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26809999346733093},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2678000032901764},{"id":"https://openalex.org/C92446256","wikidata":"https://www.wikidata.org/wiki/Q3306762","display_name":"Data validation","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.25999999046325684},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C95922358","wikidata":"https://www.wikidata.org/wiki/Q5432725","display_name":"False positive rate","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cluster59342.2025.11186479","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cluster59342.2025.11186479","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Cluster Computing (CLUSTER)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4646912219","display_name":"OAC Core: Improving Data Integrity for HPC Datasets using Sparsity Profile","funder_award_id":"2312982","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1584278023","https://openalex.org/W1652659619","https://openalex.org/W1971520093","https://openalex.org/W1980321276","https://openalex.org/W1981252059","https://openalex.org/W1989653583","https://openalex.org/W1990832096","https://openalex.org/W2013062050","https://openalex.org/W2035080386","https://openalex.org/W2043670592","https://openalex.org/W2065607954","https://openalex.org/W2083613288","https://openalex.org/W2088866486","https://openalex.org/W2114977680","https://openalex.org/W2122948532","https://openalex.org/W2126004407","https://openalex.org/W2128511938","https://openalex.org/W2296204683","https://openalex.org/W2343351966","https://openalex.org/W2373548497","https://openalex.org/W2391342772","https://openalex.org/W2418331349","https://openalex.org/W2485331474","https://openalex.org/W2529449928","https://openalex.org/W2563149920","https://openalex.org/W2580243656","https://openalex.org/W2609265534","https://openalex.org/W2647773517","https://openalex.org/W2741159018","https://openalex.org/W2749566333","https://openalex.org/W2758423835","https://openalex.org/W2767694495","https://openalex.org/W2770542984","https://openalex.org/W2794952988","https://openalex.org/W2883180703","https://openalex.org/W2899347572","https://openalex.org/W2922164696","https://openalex.org/W3016832937","https://openalex.org/W3171842021","https://openalex.org/W4230550535","https://openalex.org/W4233447799","https://openalex.org/W4248621761","https://openalex.org/W4287322404","https://openalex.org/W4318147817","https://openalex.org/W4360831835","https://openalex.org/W4385830067","https://openalex.org/W4387321084","https://openalex.org/W4396949736"],"related_works":[],"abstract_inverted_index":{"Silent":[0],"Data":[1],"Corruptions":[2],"(SDCs),":[3],"which":[4,104],"can":[5,16,197],"manifest":[6],"at":[7,150],"the":[8,61,151,156,167,200,220,239],"application":[9,152],"level":[10],"despite":[11],"extensive":[12,141],"screening":[13],"and":[14,88,181,214,243],"testing,":[15],"disrupt":[17],"meaningful":[18],"scientific":[19,99],"interpretation,":[20],"thereby":[21],"necessitating":[22],"robust":[23],"monitoring":[24],"tools":[25],"capable":[26],"of":[27,111,124,143,158,175,202],"detecting":[28],"them.":[29],"While":[30],"prior":[31,46],"approaches":[32,58],"have":[33],"demonstrated":[34],"competitive":[35,226],"detection":[36],"performance,":[37],"they":[38],"often":[39],"require":[40],"nontrivial":[41],"modifications":[42],"to":[43,55,117,135,183,219],"algorithms":[44],"or":[45,51],"knowledge,":[47],"such":[48],"as":[49,107],"spatial":[50],"temporal":[52],"data":[53,133],"patterns,":[54],"make":[56],"those":[57],"effective.":[59],"Furthermore,":[60],"error":[62,144,235,241],"model":[63],"through":[64,190],"standard":[65,178],"random":[66],"bit":[67],"flips":[68],"may":[69],"not":[70],"reflect":[71],"realistic":[72,147,234],"scenarios,":[73],"potentially":[74],"including":[75,177],"relatively":[76],"easy-to-detect":[77],"cases":[78],"with":[79,121,163,204,232],"obvious":[80],"deviations.":[81],"In":[82],"this":[83],"work,":[84],"we":[85,171],"study":[86],"SDCs":[87,203],"their":[89],"effects":[90],"on":[91,139,238],"sparse":[92,125],"matrix":[93,126],"computations,":[94],"prevalent":[95],"kernels":[96],"in":[97],"many":[98],"applications,":[100],"using":[101,128],"hardware":[102,160,169,195],"counters,":[103,170],"could":[105],"serve":[106],"a":[108,122,129,205,225,233],"holistic":[109],"indicator":[110],"revealing":[112],"program":[113,253],"behavior":[114],"changes":[115],"due":[116],"SDCs.":[118,186],"We":[119,154],"experiment":[120],"set":[123],"benchmarks":[127],"method":[130],"that":[131,194],"simulates":[132],"corruption":[134],"varying":[136],"degrees":[137],"based":[138,237],"our":[140],"analysis":[142],"propagation,":[145],"creating":[146],"SDC":[148],"occurrences":[149],"level.":[153],"detail":[155],"process":[157],"sampling":[159],"performance":[161],"counters":[162,196],"minimal":[164],"disturbance.":[165],"Using":[166],"collected":[168],"train":[172],"various":[173],"classes":[174],"classifiers,":[176],"ML,":[179],"neural-network-based,":[180],"unsupervised,":[182],"accurately":[184],"detect":[185,199],"Our":[187,222],"experimental":[188],"evaluations":[189],"k-fold":[191],"crossvalidation":[192],"indicate":[193],"effectively":[198],"presence":[201],"low":[206,244],"false":[207],"positive":[208],"rate,":[209],"incurring":[210],"comparable":[211],"training":[212],"overheads":[213],"minor":[215],"inference":[216],"overhead":[217,246],"compared":[218],"state-of-the-art.":[221],"approach":[223],"achieves":[224],"average":[227],"recall":[228],"(<tex":[229,247],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[230,248],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$&gt;0.91$</tex>)":[231],"rate":[236],"observed":[240],"propagation":[242],"runtime":[245],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$&lt;2":[249],"\\%$</tex>)":[250],"while":[251],"avoiding":[252],"modifications.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}