{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T22:38:16Z","timestamp":1778279896523,"version":"3.51.4"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T00:00:00Z","timestamp":1738022400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T00:00:00Z","timestamp":1738022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021ZD0110603"],"award-info":[{"award-number":["2021ZD0110603"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271456"],"award-info":[{"award-number":["62271456"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-024-06778-3","type":"journal-article","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T08:29:35Z","timestamp":1738052975000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["AISBench: an performance benchmark for AI server systems"],"prefix":"10.1007","volume":"81","author":[{"given":"Jian","family":"Dong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Bao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqi","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuze","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binbin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,28]]},"reference":[{"key":"6778_CR1","unstructured":"IEEE SA (2022) IEEE standard for performance benchmarking for artificial intelligence server systems"},{"key":"6778_CR2","unstructured":"Silvano C, Ielmini D, Ferrandi F et al (2023) A survey on deep learning hardware accelerators for heterogeneous HPC platforms. arXiv:2306.15552 [cs.AR]"},{"issue":"3","key":"6778_CR3","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/MDAT.2022.3161126","volume":"39","author":"S Bavikadi","year":"2022","unstructured":"Bavikadi S, Dhavlle A, Ganguly A (2022) A survey on machine learning accelerators and evolutionary hardware platforms. IEEE Des Test 39(3):91\u2013116. https:\/\/doi.org\/10.1109\/MDAT.2022.3161126","journal-title":"IEEE Des Test"},{"key":"6778_CR4","unstructured":"Hassanpour M, Riera M, Gonzalez A (2021) A survey of near-data processing architectures for neural networks. CoRR, arXiv:2112.12630"},{"issue":"12","key":"6778_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3571157","volume":"55","author":"J Gao","year":"2023","unstructured":"Gao J, Ji W, Chang F, Han S, Wei B, Liu Z, Wang Y (2023) A systematic survey of general sparse matrix-matrix multiplication. ACM Comput Surv 55(12):1\u201336. https:\/\/doi.org\/10.1145\/3571157","journal-title":"ACM Comput Surv"},{"key":"6778_CR6","unstructured":"Menghani G (2021) Efficient deep learning: a survey on making deep learning models smaller, faster, and better. CoRR, arXiv:2106.08962"},{"key":"6778_CR7","doi-asserted-by":"crossref","unstructured":"Feng S, Hou B, Jin H, et al (2022) TensorIR: an abstraction for automatic tensorized program optimization","DOI":"10.1145\/3575693.3576933"},{"key":"6778_CR8","doi-asserted-by":"publisher","unstructured":"Park K, Saur K, Banda D et al (2022) End-to-end optimization of machine learning prediction queries. In: Proceedings of the 2022 international conference on management of data. SIGMOD\/PODS \u201922. ACM. https:\/\/doi.org\/10.1145\/3514221.3526141","DOI":"10.1145\/3514221.3526141"},{"key":"6778_CR9","unstructured":"Mattson P, Cheng C, Coleman C et al (2019) MLPerf training benchmark. CoRR, arxiv:abs\/1910.01500"},{"key":"6778_CR10","unstructured":"Reddi VP, Cheng C, Kanter D et al (2019) MLPerf inference benchmark. CoRR, arxiv:abs\/1911.02549"},{"key":"6778_CR11","unstructured":"EEMBC (2019) Embedded microprocessor benchmark consortium. https:\/\/www.eembc.org\/"},{"key":"6778_CR12","doi-asserted-by":"crossref","unstructured":"Ignatov A, Timofte R, Kulik A et al (2019) AI benchmark: all about deep learning on smartphones in 2019","DOI":"10.1109\/ICCVW.2019.00447"},{"key":"6778_CR13","doi-asserted-by":"crossref","unstructured":"Ren Z, Liu Y, Shi T et al (2021) AIPerf: automated machine learning as an AI-HPC benchmark","DOI":"10.26599\/BDMA.2021.9020004"},{"key":"6778_CR14","unstructured":"Coleman C, Narayanan D, Kang D et al (2017) DAWNBench: an end-to-end deep learning benchmark and competition. NIPS ML systems workshop, arxiv:abs\/1907.10701"},{"key":"6778_CR15","doi-asserted-by":"crossref","unstructured":"Jiang Z, Gao W, Wang L et al (2019) HPC AI500: a benchmark suite for HPC AI systems","DOI":"10.1007\/978-3-030-32813-9_2"},{"key":"6778_CR16","unstructured":"Gao W, Tang F, Wang L et al (2019) AIBench: an industry standard internet service AI benchmark suite"},{"issue":"2","key":"6778_CR17","doi-asserted-by":"publisher","first-page":"376","DOI":"10.3390\/en14020376","volume":"14","author":"M \u0160pe\u0165ko","year":"2021","unstructured":"\u0160pe\u0165ko M, Vysock\u00fd O, Jans\u00edk B (2021) DGX-A100 face to face DGX-2\u2014performance, power and thermal behavior evaluation. Energies 14(2):376. https:\/\/doi.org\/10.3390\/en14020376","journal-title":"Energies"},{"key":"6778_CR18","unstructured":"GB\/T 32910.3-2016 data center-resource utilization-Part 3: electric energy usage effectiveness requirements and measuring"},{"key":"6778_CR19","unstructured":"SJ Z (2019) Research and implementation of deep learning heterogeneous computing platform based on CPU and multi-FPGA architecture. PhD thesis, Beijing University of Posts and Telecommunications"},{"key":"6778_CR20","unstructured":"Yang\u00a0J, Meng\u00a0M (2021) Research on heterogeneous task scheduling algorithm for distributed training. Comput Eng Sci 43(7)"},{"issue":"8","key":"6778_CR21","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/2.402073","volume":"28","author":"R Giladi","year":"1995","unstructured":"Giladi R, Ahitav N (1995) SPEC as a performance evaluation measure. Computer 28(8):33\u201342. https:\/\/doi.org\/10.1109\/2.402073","journal-title":"Computer"},{"key":"6778_CR22","unstructured":"ISO\/IEC\/IEEE 15288:2015 systems and software engineering-system life cycle processes"},{"key":"6778_CR23","unstructured":"H H (2000) The relationship between arithmetic mean, geometric mean and harmonic mean. J Chongqing Ind Polytech Coll 3:60\u201361"},{"key":"6778_CR24","unstructured":"ISO\/IEC 30134-2:2016 information technology-data centers-key performance indicators-part 2: power usage effectiveness (PUE)"},{"key":"6778_CR25","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. CoRR, arXiv:1512.03385","DOI":"10.1109\/CVPR.2016.90"},{"key":"6778_CR26","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R et al (2009) ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"6778_CR27","unstructured":"Gotmare A, Keskar NS, Xiong C, Socher R (2018) A closer look at deep learning heuristics: learning rate restarts, warmup and distillation. CoRR, arXiv:1810.13243"},{"key":"6778_CR28","unstructured":"Abadi M, Agarwal A, Barham P et al (2016) TensorFlow: large-scale machine learning on heterogeneous distributed systems"},{"key":"6778_CR29","doi-asserted-by":"publisher","unstructured":"Yoshida K, Sageyama R, Miwa S, Yamaki H, Honda H (2023) Analyzing performance and power-efficiency variations among NVIDIA GPUs. In: Proceedings of the 51st international conference on parallel processing. ICPP \u201922. Association for Computing Machinery, New York. https:\/\/doi.org\/10.1145\/3545008.3545084","DOI":"10.1145\/3545008.3545084"},{"key":"6778_CR30","unstructured":"Travers M(2015) CPU power consumption experiments and results analysis of intel i7-4820k. PhD thesis, Newcastle University"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06778-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06778-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06778-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T08:29:47Z","timestamp":1738052987000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06778-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,28]]},"references-count":30,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["6778"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06778-3","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,28]]},"assertion":[{"value":"30 July 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no conflict of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This paper does not involve any animal or human studies.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"441"}}