LEADER 01775nam  2200373   450 
001 996574979603316
005 20231205170137.0
010   $a1-5044-8854-7
035   $a(IEEE)9930946
035   $a(CKB)25219802500041
035   $a(NjHacI)9925219802500041
035   $a(EXLCZ)9925219802500041
100   $a20231205d2022      uy             0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$a"2937-2022 - IEEE Standard for Performance Benchmarking for Artificial Intelligence Server Systems" /$fIEEE
210  1$aNew York :$cIEEE,$d2022.
210  4$dİ2022
215   $a1 online resource (54 pages)
330   $aArtificial intelligence (AI) computing differs from generic computing in terms of device formation, operators, and usage. AI server systems, including AI server, cluster, and high-performance computing (HPC) infrastructures are designed specifically for this purpose. The performance of these infrastructures is important to users not only on generic models but also on the ones for specific domains. Formal methods for the performance benchmarking for AI server systems are provided in this standard, including approaches for test, metrics, and measure. In addition, the technical requirements for benchmarking tools are discussed.
606   $aArtificial intelligence
606   $aHigh performance computing
615  0$aArtificial intelligence.
615  0$aHigh performance computing.
676   $a006.3
801  0$bNjHacI
801  1$bNjHacl
906   $aDOCUMENT
912   $a996574979603316
996   $a"2937-2022 - IEEE Standard for Performance Benchmarking for Artificial Intelligence Server Systems"$93881733
997   $aUNISA