{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:38:06Z","timestamp":1769560686956,"version":"3.49.0"},"publisher-location":"Cham","reference-count":64,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030328122","type":"print"},{"value":"9783030328139","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-32813-9_2","type":"book-chapter","created":{"date-parts":[[2019,10,15]],"date-time":"2019-10-15T15:01:33Z","timestamp":1571151693000},"page":"10-22","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["HPC AI500: A Benchmark Suite for HPC AI Systems"],"prefix":"10.1007","author":[{"given":"Zihan","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanling","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingwang","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunjie","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hainan","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyi","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunquan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengzhong","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenli","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijia","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfeng","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,8]]},"reference":[{"key":"2_CR1","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)"},{"key":"2_CR2","unstructured":"http:\/\/www.image-net.org\/"},{"key":"2_CR3","unstructured":"Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: OSDI, vol. 16 (2016)"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia. ACM (2014)","DOI":"10.1145\/2647868.2654889"},{"issue":"11","key":"2_CR5","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1145\/2996864","volume":"59","author":"Y Chen","year":"2016","unstructured":"Chen, Y., et al.: DianNao family: energy-efficient hardware accelerators for machine learning. Commun. ACM 59(11), 105\u2013112 (2016)","journal-title":"Commun. ACM"},{"key":"2_CR6","unstructured":"Jouppi, N.P., et al.: In-datacenter performance analysis of a tensor processing unit. In: 2017 ACM\/IEEE 44th Annual International Symposium on Computer Architecture (ISCA). IEEE (2017)"},{"key":"2_CR7","unstructured":"Robert, A., et al.: Fathom: reference workloads for modern deep learning methods. In: 2016 IEEE International Symposium on Workload Characterization (IISWC). IEEE (2016)"},{"issue":"101","key":"2_CR8","first-page":"102","volume":"100","author":"C Coleman","year":"2017","unstructured":"Coleman, C., et al.: DAWNBench: an end-to-end deep learning benchmark and competition. Training 100(101), 102 (2017)","journal-title":"Training"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Zhu, H., et al.: TBD: benchmarking and analyzing deep neural network training arXiv preprint arXiv:1803.06905 (2018)","DOI":"10.1109\/IISWC.2018.8573476"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Shi, S., et al.: Benchmarking state-of-the-art deep learning software tools. In: 2016 7th International Conference on Cloud Computing and Big Data (CCBD). IEEE (2016)","DOI":"10.1109\/CCBD.2016.029"},{"key":"2_CR11","volume-title":"Computer Architecture: A Quantitative Approach","author":"JL Hennessy","year":"2011","unstructured":"Hennessy, J.L., Patterson, D.A.: Computer Architecture: A Quantitative Approach. Elsevier, Amsterdam (2011)"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: BigDataBench: a big data benchmark suite from internet services. In: 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA). IEEE (2014)","DOI":"10.1109\/HPCA.2014.6835958"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Jia, Z., Wang, L., Zhan, J., et al.: Characterizing data analysis workloads in data centers. In: 2013 IEEE International Symposium on Workload Characterization (IISWC), pp. 66\u201376. IEEE (2013)","DOI":"10.1109\/IISWC.2013.6704671"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Hao, T., Huang, Y., Wen, X., et al.: Edge AIBench: towards comprehensive end-to-end edge computing benchmarking. In: 2018 BenchCouncil International Symposium on Benchmarking, Measuring and Optimizing (Bench18) (2018)","DOI":"10.1007\/978-3-030-32813-9_3"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Luo, C., Zhang, F., Huang, C., Xiong, X., Chen, J., et al.: AIoT Bench: towards comprehensive benchmarking mobile and embedded device intelligence. In: 2018 BenchCouncil International Symposium on Benchmarking, Measuring and Optimizing (Bench18) (2018)","DOI":"10.1007\/978-3-030-32813-9_4"},{"key":"2_CR16","unstructured":"Gao, W., Tang, F., Wang, L., Zhan, J., et al.: AIBench: an industry standard internet service AI benchmark suite. Technical report (2019)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Gao, W., Luo, C., Wang, L., Xiong, X., et al.: AIBench: towards scalable and comprehensive datacenter AI benchmarking. In: 2018 BenchCouncil International Symposium on Benchmarking, Measuring and Optimizing (Bench18) (2018)","DOI":"10.1007\/978-3-030-32813-9_1"},{"key":"2_CR18","unstructured":"Dean, J.: Keynote: Large Scale Deep Learning"},{"key":"2_CR19","unstructured":"Collobert, R., Bengio, S., Marithoz, J.: Torch: a modular machine learning software library, no. EPFL-REPORT-82802. Idiap (2002)"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Kurth, T., Treichler, S., Romero, J., et al.: Exascale deep learning for climate analytics. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, p. 51. IEEE Press (2018)","DOI":"10.1109\/SC.2018.00054"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Kurth, T., Zhang, J., Satish, N., et al.: Deep learning at 15pf: supervised and semi-supervised classification for scientific data. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, p. 7. ACM (2017)","DOI":"10.1145\/3126908.3126916"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Mathuriya, A., Bard, D., Mendygral, P., et al.: CosmoFlow: using deep learning to learn the universe at scale. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, p. 65. IEEE Press (2018)","DOI":"10.1109\/SC.2018.00068"},{"key":"2_CR24","unstructured":"https:\/\/www.oreilly.com\/ideas\/a-look-at-deep-learning-for-science"},{"issue":"4","key":"2_CR25","first-page":"042034","volume":"1085","author":"W Bhimji","year":"2018","unstructured":"Bhimji, W., Farrell, S.A., Kurth, T., et al.: Deep neural networks for physics analysis on low-level whole-detector data at the LHC. J. Phys.: Conf. Ser. 1085(4), 042034 (2018)","journal-title":"J. Phys.: Conf. Ser."},{"key":"2_CR26","unstructured":"Ravanbakhsh, S., Oliva J.B., Fromenteau, S., et al.: Estimating cosmological parameters from the dark matter distribution, pp. 2407\u20132416. In: ICML (2016)"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Chen, T., Chen, Y., Duranton, M., et al.: BenchNN: On the broad potential application scope of hardware neural network accelerators. In: 2012 IEEE International Symposium on Workload Characterization (IISWC), pp. 36\u201345. IEEE (2012)","DOI":"10.1109\/IISWC.2012.6402898"},{"key":"2_CR29","unstructured":"https:\/\/mlperf.org\/"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Ben-Nun, T., Besta, M., Huber, S., et al.: A modular benchmarking infrastructure for high-performance and reproducible deep learning. arXiv preprint arXiv:1901.10183 (2019)","DOI":"10.1109\/IPDPS.2019.00018"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Patton, R.M., Johnston, J.T., Young, S.R., et al.: 167-PFlops deep learning for electron microscopy: from learning physics to atomic manipulation. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, p. 50. IEEE Press (2018)","DOI":"10.1109\/SC.2018.00053"},{"key":"2_CR32","unstructured":"Li, M., Andersen, D.G., Park, J.W., et al.: Scaling distributed machine learning with the parameter server. In: 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14), pp. 583\u2013598 (2014)"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Ravanbakhsh, S., Lanusse, F., Mandelbaum, R., et al.: Enabling dark energy with deep generative models of galaxy images. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10755"},{"key":"2_CR34","unstructured":"Mustafa, M., Bard, D., Bhimji, W., et al.: Creating virtual universes using generative adversarial networks. arXiv preprint arXiv:1706.02390 (2017)"},{"key":"2_CR35","unstructured":"Schmelzle, J., Lucchi, A., Kacprzak, T., et al.: Cosmological model discrimination with deep learning. arXiv preprint arXiv:1707.05167 (2017)"},{"issue":"3","key":"2_CR36","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1016\/0168-9002(89)91300-4","volume":"279","author":"C Peterson","year":"1989","unstructured":"Peterson, C.: Track finding with neural networks. Nucl. Instrum. Methods Phys. Res. Sect. A: Accel. Spectrom. Detect. Assoc. Equip. 279(3), 537\u2013545 (1989)","journal-title":"Nucl. Instrum. Methods Phys. Res. Sect. A: Accel. Spectrom. Detect. Assoc. Equip."},{"issue":"3","key":"2_CR37","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/0010-4655(88)90004-5","volume":"49","author":"B Denby","year":"1988","unstructured":"Denby, B.: Neural networks and cellular automata in experimental high energy physics. Comput. Phys. Commun. 49(3), 429\u2013448 (1988)","journal-title":"Comput. Phys. Commun."},{"issue":"7","key":"2_CR38","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/JHEP07(2016)069","volume":"2016","author":"L de Oliveira","year":"2016","unstructured":"de Oliveira, L., Kagan, M., Mackey, L., et al.: Jet-images-deep learning edition. J. High Energy Phys. 2016(7), 69 (2016)","journal-title":"J. High Energy Phys."},{"issue":"1","key":"2_CR39","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1007\/JHEP01(2017)110","volume":"2017","author":"PT Komiske","year":"2017","unstructured":"Komiske, P.T., Metodiev, E.M., Schwartz, M.D.: Deep learning in color: towards automated quark\/gluon jet discrimination. J. High Energy Phys. 2017(1), 110 (2017)","journal-title":"J. High Energy Phys."},{"key":"2_CR40","unstructured":"Liu, Y., Racah, E., Correa, J., et al.: Application of deep convolutional neural networks for detecting extreme weather in climate datasets. arXiv preprint arXiv:1605.01156 (2016)"},{"key":"2_CR41","unstructured":"Hong, S., Kim, S., Joh, M., et al.: GlobeNet: convolutional neural networks for typhoon eye tracking from remote sensing imagery. arXiv preprint arXiv:1708.03417 (2017)"},{"key":"2_CR42","unstructured":"Racah, E., Beckham, C., Maharaj, T., et al.: ExtremeWeather: a large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. In: Advances in Neural Information Processing Systems, pp. 3402\u20133413 (2017)"},{"issue":"2","key":"2_CR43","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","volume":"4","author":"R Gmez-Bombarelli","year":"2018","unstructured":"Gmez-Bombarelli, R., Wei, J.N., Duvenaud, D., et al.: Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4(2), 268\u2013276 (2018)","journal-title":"ACS Cent. Sci."},{"key":"2_CR44","unstructured":"https:\/\/www.ecowatch.com\/un-extreme-weather-climate-change-2633131018.html"},{"key":"2_CR45","unstructured":"https:\/\/www.cbsnews.com\/news\/extreme-weather-events-2018-top-3-most-expensive-climate-driven-events-took-place-in-us\/"},{"key":"2_CR46","unstructured":"https:\/\/extremeweatherdataset.github.io\/"},{"key":"2_CR47","unstructured":"http:\/\/stanford.edu\/group\/stanford_atlas\/"},{"issue":"1\u20132","key":"2_CR48","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/0550-3213(95)00379-7","volume":"453","author":"M Spira","year":"1995","unstructured":"Spira, M., Djouadi, A., Graudenz, D., et al.: Higgs boson production at the LHC. Nucl. Phys. B 453(1\u20132), 17\u201382 (1995)","journal-title":"Nucl. Phys. B"},{"key":"2_CR49","unstructured":"https:\/\/en.wikipedia.org\/wiki\/Cosmology"},{"key":"2_CR50","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"issue":"05","key":"2_CR51","doi-asserted-by":"publisher","first-page":"026","DOI":"10.1088\/1126-6708\/2006\/05\/026","volume":"2006","author":"T Sjstrand","year":"2006","unstructured":"Sjstrand, T., Mrenna, S., Skands, P.: PYTHIA 6.4 physics and manual. J. High Energy Phys. 2006(05), 026 (2006)","journal-title":"J. High Energy Phys."},{"key":"2_CR52","unstructured":"https:\/\/www-n.oca.eu\/ohahn\/MUSIC\/"},{"key":"2_CR53","unstructured":"https:\/\/bitbucket.org\/tassev\/pycola\/"},{"key":"2_CR54","unstructured":"https:\/\/en.wikipedia.org\/wiki\/Convolution"},{"key":"2_CR55","unstructured":"Mathuriya, A., Kurth, T., Rane, V., et al.: Scaling GRPC tensorflow on 512 nodes of cori supercomputer. arXiv preprint arXiv:1712.09388 (2017)"},{"key":"2_CR56","unstructured":"Sergeev, A., Del Balso, M.: Horovod: fast and easy distributed deep learning in TensorFlow. arXiv preprint arXiv:1802.05799 (2018)"},{"key":"2_CR57","unstructured":"Gibiansky, A.: Bringing HPC techniques to deep learning (2017). http:\/\/research.baidu.com\/bringing-hpc-techniques-deep-learning. Accessed 6 Dec 2017"},{"key":"2_CR58","unstructured":"https:\/\/www.open-mpi.org\/"},{"key":"2_CR59","unstructured":"https:\/\/www.jlab.org\/indico\/event\/247\/session\/8\/contribution\/30\/material\/slides\/0.pdf"},{"key":"2_CR60","unstructured":"Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (2015)"},{"key":"2_CR61","doi-asserted-by":"crossref","unstructured":"Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"2_CR62","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"2_CR63","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"2_CR64","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"}],"container-title":["Lecture Notes in Computer Science","Benchmarking, Measuring, and Optimizing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32813-9_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T20:54:24Z","timestamp":1664657664000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-32813-9_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030328122","9783030328139"],"references-count":64,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32813-9_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"8 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Bench","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Benchmarking, Measuring and Optimization","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Seattle, WA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bench2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/prof.ict.ac.cn\/Bench18\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CyberDhair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}