{"id":"https://openalex.org/W4396686500","doi":"https://doi.org/10.1145/3629527.3652273","title":"EchoSwift: An Inference Benchmarking and Configuration Discovery Tool for Large Language Models (LLMs)","display_name":"EchoSwift: An Inference Benchmarking and Configuration Discovery Tool for Large Language Models (LLMs)","publication_year":2024,"publication_date":"2024-05-07","ids":{"openalex":"https://openalex.org/W4396686500","doi":"https://doi.org/10.1145/3629527.3652273"},"language":"en","primary_location":{"id":"doi:10.1145/3629527.3652273","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3629527.3652273","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3629527.3652273","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 15th ACM/SPEC International Conference on Performance Engineering","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3629527.3652273","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101715864","display_name":"K Vamsi Krishna","orcid":"https://orcid.org/0009-0002-6638-7431"},"institutions":[{"id":"https://openalex.org/I110675161","display_name":"Infosys (India)","ror":"https://ror.org/03bs18y54","country_code":"IN","type":"company","lineage":["https://openalex.org/I110675161"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Karthik Krishna","raw_affiliation_strings":["CTO, Infobell IT Solutions Pvt Ltd, Bengaluru, Karnataka, India"],"raw_orcid":"https://orcid.org/0009-0002-6638-7431","affiliations":[{"raw_affiliation_string":"CTO, Infobell IT Solutions Pvt Ltd, Bengaluru, Karnataka, India","institution_ids":["https://openalex.org/I110675161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5096717918","display_name":"Ramana Bandili","orcid":"https://orcid.org/0009-0006-9084-9113"},"institutions":[{"id":"https://openalex.org/I110675161","display_name":"Infosys (India)","ror":"https://ror.org/03bs18y54","country_code":"IN","type":"company","lineage":["https://openalex.org/I110675161"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ramana Bandili","raw_affiliation_strings":["CTO, Infobell IT Solutions PVt Ltd, Bangalore, India"],"raw_orcid":"https://orcid.org/0009-0006-9084-9113","affiliations":[{"raw_affiliation_string":"CTO, Infobell IT Solutions PVt Ltd, Bangalore, India","institution_ids":["https://openalex.org/I110675161"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I110675161"],"apc_list":null,"apc_paid":null,"fwci":0.2986,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.60723068,"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":"158","last_page":"162"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9825000166893005,"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/T10028","display_name":"Topic Modeling","score":0.9825000166893005,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.977400004863739,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.958899974822998,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.9292539358139038},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7138071656227112},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6057215929031372},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5521737337112427},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5098063349723816},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.49686911702156067},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.45486965775489807},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4487728178501129},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.2704728841781616},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22996070981025696},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.20089703798294067},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.10295799374580383},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07956123352050781}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.9292539358139038},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7138071656227112},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6057215929031372},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5521737337112427},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5098063349723816},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.49686911702156067},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45486965775489807},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4487728178501129},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.2704728841781616},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22996070981025696},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.20089703798294067},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.10295799374580383},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07956123352050781},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"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/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3629527.3652273","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3629527.3652273","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3629527.3652273","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 15th ACM/SPEC International Conference on Performance Engineering","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3629527.3652273","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3629527.3652273","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3629527.3652273","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 15th ACM/SPEC International Conference on Performance Engineering","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4396686500.pdf","grobid_xml":"https://content.openalex.org/works/W4396686500.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4238897586","https://openalex.org/W435179959","https://openalex.org/W2619091065","https://openalex.org/W2059640416","https://openalex.org/W1490753184","https://openalex.org/W2284465472","https://openalex.org/W2291782699","https://openalex.org/W1993948687","https://openalex.org/W2000169967","https://openalex.org/W2112883198"],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"are":[4,11,27,227],"advanced":[5,40],"natural":[6],"language":[7,41],"processing":[8],"models":[9,26],"that":[10,121,188,224],"trained":[12],"on":[13],"vast":[14],"amounts":[15],"of":[16,47,84,95,108,147],"text":[17],"data":[18],"to":[19,29,79,92,133,186,235],"understand":[20,30],"and":[21,34,38,61,113,165,178,205],"generate":[22,32],"human-like":[23],"language.":[24],"These":[25],"designed":[28,78],"context,":[31],"coherent":[33],"contextually":[35],"relevant":[36],"text,":[37],"demonstrate":[39],"capabilities.":[42],"In":[43],"the":[44,49,81,93,125,135,145,148,189,198],"dynamic":[45,119],"landscape":[46,120],"LLMs,":[48],"demand":[50],"for":[51,138,151,191,200],"efficient":[52,230],"inference":[53,140],"benchmarking":[54,76,129],"is":[55,116,184],"crucial.":[56],"Organizations":[57],"such":[58],"as":[59,142,144,163,168,176,181],"TPC":[60],"SPEC":[62],"brought":[63],"several":[64],"industry":[65],"standard":[66],"benchmark":[67],"[1][2][3][4].":[68],"This":[69],"publication":[70,123],"introduces":[71],"EchoSwift":[72,209],"[11],":[73],"a":[74,105,127],"comprehensive":[75,139,214],"framework":[77,130],"evaluate":[80],"real-time":[82],"performance":[83],"LLMs":[85,90],"in":[86,213],"deployment":[87],"scenarios.":[88],"As":[89],"ascend":[91],"forefront":[94],"technological":[96],"innovation,":[97],"their":[98,109,236],"seamless":[99],"integration":[100],"into":[101],"real-world":[102],"applications":[103],"demands":[104],"nuanced":[106],"understanding":[107],"efficiency,":[110],"throughput,":[111],"latency,":[112],"scalability.":[114],"It":[115,183],"within":[117],"this":[118],"our":[122],"unveils":[124],"EchoSwift,":[126],"novel":[128],"meticulously":[131],"crafted":[132],"address":[134],"pressing":[136],"need":[137,195],"benchmarking,":[141],"well":[143],"discovery":[146,216],"right":[149],"configuration":[150,190,215],"specific":[152,237],"LLM":[153,225],"requirements.":[154],"For":[155],"instance,":[156],"certain":[157],"deployments":[158,226],"might":[159,172],"have":[160,173],"32":[161],"tokens":[162,167,175,180],"input":[164,177],"256":[166,174],"output,":[169],"while":[170],"others":[171],"64":[179],"output.":[182],"crucial":[185],"acknowledge":[187],"these":[192],"two":[193],"requirements":[194],"not":[196,210,228],"be":[197],"same":[199],"an":[201],"optimal":[202],"performance,":[203],"scale":[204],"better":[206],"TCO.":[207],"The":[208],"only":[211,229],"aids":[212],"but":[217,231],"also":[218,232],"facilitates":[219],"robust":[220],"Performance/Scale":[221],"testing,":[222],"ensuring":[223],"finely":[233],"tuned":[234],"operational":[238],"demands.":[239]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}