{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T05:40:15Z","timestamp":1743572415000,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031856990","type":"print"},{"value":"9783031857003","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-85700-3_17","type":"book-chapter","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T05:02:22Z","timestamp":1743570142000},"page":"236-248","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parallelism in\u00a0GNN: Possibilities and\u00a0Limits of\u00a0Current Approaches"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2643-3483","authenticated-orcid":false,"given":"Valeria","family":"Mele","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8521-1645","authenticated-orcid":false,"given":"Luisa","family":"Carracciuolo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2640-157X","authenticated-orcid":false,"given":"Diego","family":"Romano","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","unstructured":"Abadal, S., Jain, A., Guirado, R., L\u00f3pez-Alonso, J., Alarc\u00f3n, E.: Computing graph neural networks: a survey from algorithms to accelerators. ACM Comput. Surv. 54(9) (2022). https:\/\/doi.org\/10.1145\/3477141","DOI":"10.1145\/3477141"},{"issue":"4","key":"17_CR2","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/0925-2312(93)90006-O","volume":"5","author":"SI Amari","year":"1993","unstructured":"Amari, S.I.: Backpropagation and stochastic gradient descent method. Neurocomputing 5(4), 185\u2013196 (1993). https:\/\/doi.org\/10.1016\/0925-2312(93)90006-O","journal-title":"Neurocomputing"},{"key":"17_CR3","doi-asserted-by":"publisher","unstructured":"Bertero, M., et al.: Medigrid: a medical imaging application for computational grids. In: Proceedings of the International Parallel and Distributed Processing Symposium, IPDPS 2003 (2003). https:\/\/doi.org\/10.1109\/IPDPS.2003.1213457","DOI":"10.1109\/IPDPS.2003.1213457"},{"key":"17_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2022.107898","volume":"166","author":"W Bradley","year":"2022","unstructured":"Bradley, W., et al.: Perspectives on the integration between first-principles and data-driven modeling. Comput. Chem. Eng. 166, 107898 (2022). https:\/\/doi.org\/10.1016\/j.compchemeng.2022.107898","journal-title":"Comput. Chem. Eng."},{"issue":"4","key":"17_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6008","volume":"33","author":"L Carracciuolo","year":"2021","unstructured":"Carracciuolo, L., Mele, V., Szustak, L.: About the granularity portability of block-based krylov methods in heterogeneous computing environments. Concurr. Comput. Pract. Exp. 33(4), e6008 (2021). https:\/\/doi.org\/10.1002\/cpe.6008","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Carracciuolo, L., D\u2019Amora, U.: Mathematical tools for simulation of 3d bioprinting processes on high-performance computing resources: the state of the art. Appl. Sci. 14(14) (2024). https:\/\/doi.org\/10.3390\/app14146110. https:\/\/www.mdpi.com\/2076-3417\/14\/14\/6110","DOI":"10.3390\/app14146110"},{"key":"17_CR7","doi-asserted-by":"publisher","unstructured":"Carracciuolo, L., D\u2019Amore, L., Mele, V.: Toward a fully parallel multigrid in time algorithm in petsc environment: a case study in ocean models. In: 2015 International Conference on High Performance Computing & Simulation (HPCS), pp. 595\u2013598 (2015). https:\/\/doi.org\/10.1109\/HPCSim.2015.7237098","DOI":"10.1109\/HPCSim.2015.7237098"},{"key":"17_CR8","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1090\/qam\/10667","volume":"2","author":"HB Curry","year":"1944","unstructured":"Curry, H.B.: The method of steepest descent for non-linear minimization problems. Quart. Appl. Math. 2, 258\u2013261 (1944). https:\/\/doi.org\/10.1090\/qam\/10667","journal-title":"Quart. Appl. Math."},{"key":"17_CR9","doi-asserted-by":"publisher","unstructured":"Daigavane, A., Ravindran, B., Aggarwal, G.: Understanding convolutions on graphs. Distill (2021). https:\/\/doi.org\/10.23915\/distill.00032","DOI":"10.23915\/distill.00032"},{"key":"17_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-319-32152-3_3","volume-title":"Parallel Processing and Applied Mathematics","author":"L D\u2019Amore","year":"2016","unstructured":"D\u2019Amore, L., Mele, V., Laccetti, G., Murli, A.: Mathematical approach to the performance evaluation of matrix multiply algorithm. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds.) PPAM 2015. LNCS, vol. 9574, pp. 25\u201334. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-32152-3_3"},{"key":"17_CR11","doi-asserted-by":"publisher","unstructured":"Fu, Q., Ji, Y., Huang, H.H.: Tlpgnn: a lightweight two-level parallelism paradigm for graph neural network computation on gpu. In: Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing (HPDC \u201922), pp. 122\u2013134. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3502181.3531467","DOI":"10.1145\/3502181.3531467"},{"key":"17_CR12","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS\u201917, pp. 1025\u20131035. Curran Associates Inc., Red Hook (2017). https:\/\/dl.acm.org\/doi\/10.5555\/3294771.3294869"},{"key":"17_CR13","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/3-540-44668-0_13","volume-title":"Artificial Neural Networks \u2013 ICANN 2001","author":"S Hochreiter","year":"2001","unstructured":"Hochreiter, S., Younger, A.S., Conwell, P.R.: Learning to learn using gradient descent. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) Artificial Neural Networks \u2013 ICANN 2001, pp. 87\u201394. Springer, Heidelberg (2001)"},{"key":"17_CR14","doi-asserted-by":"publisher","first-page":"6973","DOI":"10.1007\/s00521-021-06822-w","volume":"34","author":"S Izzo","year":"2022","unstructured":"Izzo, S., Prezioso, E., Giampaolo, F., et al.: Classification of urban functional zones through deep learning. Neural Comput. Appl. 34, 6973\u20136990 (2022). https:\/\/doi.org\/10.1007\/s00521-021-06822-w","journal-title":"Neural Comput. Appl."},{"key":"17_CR15","doi-asserted-by":"publisher","unstructured":"Khamsi, M.A., Kirk, W.A.: An Introduction to Metric Spaces and Fixed Point Theory. John Wiley & Sons, Ltd., Hoboken (2001). https:\/\/doi.org\/10.1002\/9781118033074","DOI":"10.1002\/9781118033074"},{"key":"17_CR16","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017). https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Klimke, M., V\u00f6lz, B., Buchholz, M.: Cooperative behavior planning for automated driving using graph neural networks. In: 2022 IEEE Intelligent Vehicles Symposium (IV), pp. 167\u2013174. IEEE (2022)","DOI":"10.1109\/IV51971.2022.9827230"},{"key":"17_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/978-3-030-43222-5_9","volume-title":"Parallel Processing and Applied Mathematics","author":"M Lapegna","year":"2020","unstructured":"Lapegna, M., Mele, V., Romano, D.: An adaptive strategy for dynamic data clustering with the k-means algorithm. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K. (eds.) PPAM 2019. LNCS, vol. 12044, pp. 101\u2013110. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-43222-5_9"},{"key":"17_CR19","doi-asserted-by":"publisher","unstructured":"Maddalena, L., Petrosino, A., Laccetti, G.: A fusion-based approach to digital movie restoration. Pattern Recogn. 42(7), 1485\u20131495 (2009). https:\/\/doi.org\/10.1016\/j.patcog.2008.10.026. https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0031320308004639","DOI":"10.1016\/j.patcog.2008.10.026"},{"key":"17_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-319-78054-2_2","volume-title":"Parallel Processing and Applied Mathematics","author":"L Marcellino","year":"2018","unstructured":"Marcellino, L., et al.: Using GPGPU accelerated interpolation algorithms for marine bathymetry processing with on-premises and cloud based computational resources. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds.) PPAM 2017. LNCS, vol. 10778, pp. 14\u201324. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-78054-2_2"},{"issue":"24","key":"17_CR21","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4928","volume":"30","author":"V Mele","year":"2018","unstructured":"Mele, V., Constantinescu, E., Carracciuolo, L., D\u2019amore, L.: A petsc parallel-in-time solver based on mgrit algorithm. Concurr. Comput. Pract. Exp. 30(24), e4928 (2018). https:\/\/doi.org\/10.1002\/cpe.4928","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"17_CR22","unstructured":"Nielsen, M.: Neural Networks and Deep Learning. Determination Press (2016). http:\/\/neuralnetworksanddeeplearning.com\/index.html"},{"key":"17_CR23","unstructured":"Poliak, B.: Introduction to Optimization. Translations series in mathematics and engineering, Optimization Software, Publications Division (1987)"},{"issue":"4","key":"17_CR24","doi-asserted-by":"publisher","first-page":"3107","DOI":"10.1109\/JIOT.2021.3118834","volume":"10","author":"E Prezioso","year":"2023","unstructured":"Prezioso, E., Giampaolo, F., Mazzocca, C., Bujari, A., Mele, V., Amato, F.: Machine learning insights for behavioral data analysis supporting the autonomous vehicles scenario. IEEE Internet Things J. 10(4), 3107\u20133117 (2023). https:\/\/doi.org\/10.1109\/JIOT.2021.3118834","journal-title":"IEEE Internet Things J."},{"key":"17_CR25","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1016\/j.future.2020.06.027","volume":"112","author":"D Romano","year":"2020","unstructured":"Romano, D., Lapegna, M., Mele, V., Laccetti, G.: Designing a gpu-parallel algorithm for raw sar data compression: a focus on parallel performance estimation. Futur. Gener. Comput. Syst. 112, 695\u2013708 (2020). https:\/\/doi.org\/10.1016\/j.future.2020.06.027","journal-title":"Futur. Gener. Comput. Syst."},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533\u2013536 (1986). https:\/\/api.semanticscholar.org\/CorpusID:205001834","DOI":"10.1038\/323533a0"},{"issue":"1","key":"17_CR27","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2009). https:\/\/doi.org\/10.1109\/TNN.2008.2005605","journal-title":"IEEE Trans. Neural Netw."},{"key":"17_CR28","doi-asserted-by":"publisher","unstructured":"Schatz, M.D., Geijn, R.A., Poulson, J.: Parallel matrix multiplication: a systematic journey. SIAM J. Sci. Comput. 38 (2016). https:\/\/doi.org\/10.1137\/140993478","DOI":"10.1137\/140993478"},{"key":"17_CR29","unstructured":"Tayal, R.: A Gentle Introduction to Distributed Training of ML Models (2023). https:\/\/medium.com\/@rachittayal7\/a-gentle-introduction-to-distributed-training-of-ml-models-81295a7057de. Accessed 19 Aug 2024"},{"key":"17_CR30","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018). https:\/\/openreview.net\/forum?id=rJXMpikCZ"},{"key":"17_CR31","doi-asserted-by":"publisher","unstructured":"von Stosch, M., Oliveira, R., Peres, J., Feyo de Azevedo, S.: Hybrid semi-parametric modeling in process systems engineering: past, present and future. Comput. Chem. Eng. 60, 86\u2013101 (2014). https:\/\/doi.org\/10.1016\/j.compchemeng.2013.08.008","DOI":"10.1016\/j.compchemeng.2013.08.008"},{"key":"17_CR32","doi-asserted-by":"crossref","unstructured":"Wang, C., Desen, S., Bai, Y.: PiPAD: pipelined and parallel dynamic GNN training on GPUs (2023)","DOI":"10.1145\/3572848.3577487"},{"key":"17_CR33","unstructured":"Wang, M., et al.: Deep graph library: towards efficient and scalable deep learning on graphs. CoRR (2019). http:\/\/arxiv.org\/abs\/1909.01315"},{"key":"17_CR34","doi-asserted-by":"publisher","unstructured":"Weinzierl, T.: The Pillars of Science, pp.\u00a03\u20139. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-76194-3_1","DOI":"10.1007\/978-3-030-76194-3_1"},{"key":"17_CR35","doi-asserted-by":"crossref","unstructured":"Wu, L., Cui, P., Pei, J., Zhao, L.: Graph neural networks: foundations, frontiers, and applications. Springer, Singapore (2022). https:\/\/graph-neural-networks.github.io\/index.html","DOI":"10.1007\/978-981-16-6054-2"},{"key":"17_CR36","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=ryGs6iA5Km"}],"container-title":["Lecture Notes in Computer Science","Parallel Processing and Applied Mathematics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-85700-3_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T05:02:36Z","timestamp":1743570156000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-85700-3_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031856990","9783031857003"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-85700-3_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"PPAM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel Processing and Applied Mathematics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ostrava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppam2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ppam.edu.pl\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}