{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T20:38:44Z","timestamp":1771706324219,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030848248","type":"print"},{"value":"9783030848255","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-84825-5_1","type":"book-chapter","created":{"date-parts":[[2021,8,15]],"date-time":"2021-08-15T20:02:35Z","timestamp":1629057755000},"page":"3-16","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Analyzing the I\/O Patterns of Deep Learning Applications"],"prefix":"10.1007","author":[{"given":"Edixon","family":"P\u00e1rraga","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Betzabeth","family":"Le\u00f3n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rom\u00e1n","family":"Bond","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diego","family":"Encinas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aprigio","family":"Bezerra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandra","family":"Mendez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dolores","family":"Rexachs","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emilio","family":"Luque","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,16]]},"reference":[{"key":"1_CR1","unstructured":"Podareanu, D., Codreanu, V., Aigner, S., van Leeuwen, C., Weinberg, V.: Best practice guide - deep learning (2019). https:\/\/prace-ri.eu\/training-support\/best-practice-guides\/best-practice-guide-deep-learning\/"},{"issue":"2\u20133","key":"1_CR2","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1023\/A:1022602019183","volume":"3","author":"DE Goldberg","year":"1988","unstructured":"Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2\u20133), 95\u201399 (1988)","journal-title":"Mach. Learn."},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Ilin, R., Watson, T., Kozma, R.: Abstraction hierarchy in deep learning neural networks. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 768\u2013774 (2017)","DOI":"10.1109\/IJCNN.2017.7965929"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: Entropy-aware I\/O pipelining for large-scale deep learning on HPC systems. In: 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 145\u2013156 (2018)","DOI":"10.1109\/MASCOTS.2018.00023"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Brinkmann, A., et al.: Ad hoc file systems for high-performance computing. J. Comput. Sci. Technol 35 (2020)","DOI":"10.1007\/s11390-020-9801-1"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)","DOI":"10.1145\/2647868.2654889"},{"key":"1_CR7","unstructured":"Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https:\/\/www.tensorflow.org\/"},{"key":"1_CR8","unstructured":"Pytorch. https:\/\/pytorch.org\/docs\/stable\/index.html\/. Accessed 24 Mar 2021"},{"key":"1_CR9","unstructured":"Intel: DAOS - Distributed Application Object Storage. Intel, Technical report (2019). https:\/\/www.intel.com\/content\/dam\/www\/public\/us\/en\/documents\/solution-briefs\/high-performance-storage-brief.pdf"},{"key":"1_CR10","unstructured":"Koziol, Q.: I\/O for deep learning at scale. NERSC, Technical report (2019). https:\/\/storageconference.us\/2019\/Invited\/Koziol.slides.pdf"},{"key":"1_CR11","unstructured":"Rojas, E., Kahira, A.N., Meneses, E., Gomez, L.B., Badia, R.M.: A study of checkpointing in large scale training of deep neural networks. arXiv preprint arXiv:2012.00825 (2020)"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Chien, S.W.D., et al.: Characterizing deep-learning i\/o workloads in tensorflow. In: 2018 IEEE\/ACM 3rd International Workshop on Parallel Data Storage Data Intensive Scalable Computing Systems (PDSW-DISCS), pp. 54\u201363 (2018)","DOI":"10.1109\/PDSW-DISCS.2018.00011"},{"key":"1_CR13","unstructured":"arconsis IT-Solutions GmbH: Firstnetworkwithprediction. Technical report. https:\/\/github.com\/arconsis\/cifar-10-with-tensorflow2"},{"issue":"3","key":"1_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2027066.2027068","volume":"7","author":"P Carns","year":"2011","unstructured":"Carns, P., et al.: Understanding and improving computational science storage access through continuous characterization. ACM Trans. Storage (TOS) 7(3), 1\u201326 (2011)","journal-title":"ACM Trans. Storage (TOS)"},{"key":"1_CR15","unstructured":"Sergeev, A., Balso, M. D.: Horovod: fast and easy distributed deep learning in TensorFlow. arXiv preprint arXiv:1802.05799 (2018)"},{"key":"1_CR16","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/github.com\/fchollet\/keras"},{"key":"1_CR17","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. Technical report (2009)"},{"key":"1_CR18","unstructured":"LeCun, Y., Cortes, C., Burges, C.: MNIST. http:\/\/yann.lecun.com\/exdb\/mnist\/. Accessed 24 Mar 2021"},{"key":"1_CR19","unstructured":"Horovod - tensorflow2$$\\_$$mnist.py. https:\/\/github.com\/horovod\/horovod\/tree\/master\/examples\/tensorflow2. Accessed 26 Mar 2021"},{"key":"1_CR20","unstructured":"LeCun, Y., Cortes, C., Burges, C.: Horovod - pytorch\\_mnist.py. https:\/\/github.com\/horovod\/horovod\/tree\/master\/examples\/pytorch. Accessed 26 Mar 2021"}],"container-title":["Communications in Computer and Information Science","Cloud Computing, Big Data &amp; Emerging Topics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-84825-5_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T11:45:10Z","timestamp":1673091910000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-84825-5_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030848248","9783030848255"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-84825-5_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"16 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"JCC-BD&ET","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference on Cloud Computing, Big Data & Emerging Topics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"La Plata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Argentina","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"jcc&bd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/jcc.info.unlp.edu.ar","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":"OJS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37","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":"12","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":"2","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":"32% - 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":"3.19","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":"1.08","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}