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Status, challenges and trends of data-intensive supercomputing

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Abstract

Supercomputing technology has been supporting the solution of cutting-edge scientific and complex engineering problems since its inception—serving as a comprehensive representation of the most advanced computer hardware and software technologies over a period of time. Over the course of nearly 80 years of development, supercomputing has progressed from being oriented towards computationally intensive tasks, to being oriented towards a hybrid of computationally and data-intensive tasks. Driven by the continuous development of high performance data analytics (HPDA) applications—such as big data, deep learning, and other intelligent tasks—supercomputing storage systems are facing challenges such as a sudden increase in data volume for computational processing tasks, increased and diversified computing power of supercomputing systems, and higher reliability and availability requirements. Based on this, data-intensive supercomputing, which is deeply integrated with data centers and smart computing centers, aims to solve the problems of complex data type optimization, mixed-load optimization, multi-protocol support, and interoperability on the storage system—thereby becoming the main protagonist of research and development today and for some time in the future. This paper first introduces key concepts in HPDA and data-intensive computing, and then illustrates the extent to which existing platforms support data-intensive applications by analyzing the most representative supercomputing platforms today (Fugaku, Summit, Sunway TaihuLight, and Tianhe 2A). This is followed by an illustration of the actual demand for data-intensive applications in today’s mainstream scientific and industrial communities from the perspectives of both scientific and commercial applications. Next, we provide an outlook on future trends and potential challenges data-intensive supercomputing is facing. In a word, this paper provides researchers and practitioners with a quick overview of the key concepts and developments in supercomputing, and captures the current and future data-intensive supercomputing research hotspots and key issues that need to be addressed.

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References

  • Abramson, D., Jin, C., Luong, J., Carroll, J.: A beegfs-based caching file system for data-intensive parallel computing. In: Asian Conference on Supercomputing Frontiers, Springer, Cham, pp. 3–22 (2020)

  • Allen, R.M.: Transforming earthquake detection? Science 335(6066), 297–298 (2012)

    Google Scholar 

  • Amin, M.S., Ahn, H.: Earthquake disaster avoidance learning system using deep learning. Cognit. Syst. Res. 66, 221–235 (2021)

    Google Scholar 

  • Anbuvizhi, R., Balakumar, V.: Credit/debit card transaction survey using map reduce in hdfs and implementing syferlock to prevent fraudulent. Int. J. Comput. Sci. Netw. Security (IJCSNS) 16(11), 106 (2016)

    Google Scholar 

  • Anh Khoa, T., Quang Minh, N., Hai Son, H., Nguyen Dang Khoa, C., Ngoc Tan, D., VanDung, N., Hoang Nam, N., Ngoc Minh Duc, D., Trung Tin, N.: Wireless sensor networks and machine learning meet climate change prediction. Int. J. Commun. Syst. 34(3), e4687 (2021)

    Google Scholar 

  • de Assuncao, M.D., da Silva, Veith A., Buyya, R.: Distributed data stream processing and edge computing: a survey on resource elasticity and future directions. J. Netwk. Comput. Appl. 103, 1–17 (2018)

    Google Scholar 

  • Belair, S., Carrera, M.L., Abrahamowicz, M., Alavi, N., Badawy, B., Shahabadi, M.B., Bilodeau, B., Charpentier, D., Deacu, D., Durnford, D., et al.: Spaceborne l-band radiometry in environment and climate change canada (eccc)’s numerical analysis and prediction systems. In: IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, pp. 7526–7528 (2019)

  • Braam, P.J., Zahir, R.: Lustre technical project summary. Attachment A to RFP B514193 Response (2001)

  • Chang, C.C., Nicholson, A.N., Rinaldi, E., Berkowitz, E., Garron, N., Brantley, D.A., Monge-Camacho, H., Monahan, C.J., Bouchard, C., Clark, M.A., et al.: A per-cent-level determination of the nucleon axial coupling from quantum chromodynamics. Nature 558(7708), 91–94 (2018)

    Google Scholar 

  • Chen, Q., Chen, K., Chen, Z.N., Xue, W., Ji, X., Yang, B.: Lessons learned from optimizing the sunway storage system for higher application i/o performance. J. Comput. Sci. Technol. 35(1), 47–60 (2020)

    Google Scholar 

  • Chien, S., Bashir, R., Nerem, R.M., Pettigrew, R.: Engineering as a new frontier for translational medicine. Sci. Trans. Med. 7(281), 281fs13 (2015)

  • Dai, Y., Yan, J., Tang, X., Zhao, H., Guo, M. Online credit card fraud detection: A hybrid framework with big data technologies. In: 2016 IEEE Trustcom/BigDataSE/ISPA, IEEE, pp. 1644–1651 (2016)

  • Dong, W., Li, K., Kang, L., Quan, Z., Li, K.: Implementing molecular dynamics simulation on the sunway taihulight system with heterogeneous many-core processors. Concurrency Comput. 30(16), e4468 (2018)

    Google Scholar 

  • Duan, X., Gao, P., Zhang, T., Zhang, M., Liu, W., Zhang, W., Xue, W., Fu, H., Gan, L., Chen, D., et al.: Redesigning lammps for peta-scale and hundred-billion-atom simulation on sunway taihulight. In: SC18: International conference for high performance computing, networking, storage and analysis, IEEE, pp. 148–159 (2018)

  • Forough, J., Momtazi, S.: Ensemble of deep sequential models for credit card fraud detection. Appl. Soft Comput. 99, 106883 (2021)

    Google Scholar 

  • Fu, H., Liao, J., Yang, J., Wang, L., Song, Z., Huang, X., Yang, C., Xue, W., Liu, F., Qiao, F., et al.: The sunway taihulight supercomputer: system and applications. Sci. China Inform. Sci 59(7), 1–16 (2016)

    Google Scholar 

  • Gao Z.-Y., Zhang L.-M., Duan: A quantum machine learning algorithm based on generative models. Sci. Adv. (2018)

  • Gao, J., Zheng, F., Qi, F., Ding, Y., Li, H., Lu, H., He, W., Wei, H., Jin, L., Liu, X., et al.: Sunway supercomputer architecture towards exascale computing: analysis and practice. Sci. China Inform. Sci. 64(4), 1–21 (2021)

    Google Scholar 

  • Gao, P., Duan, X., Zhang, T., Zhang, M., Yang, G.: Millimeter-scale and billion-atom reactive force field simulation on sunway taihulight. IEEE Transactions on Parallel and Distributed Systems PP(99), 1–1 (2020)

    Google Scholar 

  • Gianinetto, M., Frassy, F., Marchesi, A., Maianti, P., De Paulis, R., Biffi, P.G., Nodari, F.R.: Mapping large-scale microseepage signals for supporting oil and gas exploration in new ventures. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp. 5430–5433 (2016)

  • Guo, S., Qiao, W., Chen, B., Wang, B.: Prediction and abnormality analysis of climate change based on pca-arma and pcc. In: 2020 IEEE International Conference on Networking, Sensing and Control (ICNSC), IEEE, pp. 1–6 (2020)

  • Hager, G., Wellein, G.: Introduction to High Performance Computing for Scientists and Engineers. CRC Press (2010)

    Google Scholar 

  • Harchol-Balter, M.: Performance Modeling and Design of Computer Systems: Queueing Theory in Action. Cambridge University Press (2013)

    MATH  Google Scholar 

  • Henz, B.J., Elliot, L., Barton, M., Shires, D.: High-performance computing for the next generation combat vehicle. Tech. rep., US Army Research Laboratory Aberdeen Proving Ground, United States (2018)

  • Hernández, B., Somnath, S., Yin, J., Lu, H., Eaton, J., Entschev, P., Kirkham, J., Ronaghi, Z.: Performance evaluation of python based data analytics frameworks in summit: Early experiences. In: Smoky Mountains Computational Sciences and Engineering Conference, Springer, pp. 366–380 (2020)

  • Hohman, F., Park, H., Robinson, C., Chau, D.H.P.: S ummit: Scaling deep learning interpretability by visualizing activation and attribution summarizations. IEEE Trans. Vis. Comput. Graphics 26(1), 1096–1106 (2019)

    Google Scholar 

  • Hong, H.J., Chuang, J.C., Hsu, C.H.: Animation rendering on multimedia fog computing platforms. In: 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), IEEE, pp. 336–343 (2016)

  • Hu, Y., Yang, H., Luan, Z., Gan, L., Yang, G., Qian, D.: Massively scaling seismic processing on sunway taihulight supercomputer. IEEE Trans. Parallel Distributed Syst. 31(5), 1194–1208 (2019)

    Google Scholar 

  • Hush, M.R.: Machine learning for quantum physics. Science 355(6325), 580–580 (2017)

    Google Scholar 

  • Iannone, F., Ambrosino, F., Bracco, G., De Rosa, M., Funel, A., Guarnieri, G., Migliori, S., Palombi, F., Ponti, G., Santomauro, G., et al.: Cresco enea hpc clusters: a working example of a multifabric gpfs spectrum scale layout. In: 2019 International Conference on High Performance Computing & Simulation (HPCS), IEEE, pp. 1051–1052 (2019)

  • Ichimura, T., Fujita, K., Yamaguchi, T., Naruse, A., Wells, J.C., Schulthess, T.C., Straatsma, T.P., Zimmer, C.J., Martinasso, M., Nakajima, K., et al.: A fast scalable implicit solver for nonlinear time-evolution earthquake city problem on low-ordered unstructured finite elements with artificial intelligence and transprecision computing. In: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE, pp. 627–637 (2018)

  • Joubert, W., Weighill, D., Kainer, D., Climer, S., Justice, A., Fagnan, K., Jacobson, D.: Attacking the opioid epidemic: Determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction. In: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE, pp. 717–730 (2018)

  • Jun, S.P., Yoo, H.S., Choi, S.: Ten years of research change using google trends: from the perspective of big data utilizations and applications. Technol. Forecasting Soc. Change 130, 69–87 (2018)

    Google Scholar 

  • Kahle, J.A., Moreno, J., Dreps, D.: 2.1 summit and sierra: designing ai/hpc supercomputers. In: 2019 IEEE International Solid-State Circuits Conference-(ISSCC), IEEE, pp. 42–43 (2019)

  • Kappe, C.P., Böttinger, M., Leitte, H.: Analysis of decadal climate predictions with user-guided hierarchical ensemble clustering. In: Computer Graphics Forum, Wiley Online Library, vol 38, pp. 505–515 (2019)

  • Kleppmann, M.: Designing data-intensive applications: The big ideas behind reliable, scalable, and maintainable systems. ” O’Reilly Media, Inc.” (2017)

  • Kodama, Y., Odajima, T., Arima, E., Sato, M: Evaluation of power management control on the supercomputer fugaku. In: 2020 IEEE International Conference on Cluster Computing (CLUSTER), IEEE, pp. 484–493 (2020)

  • Koppers, L., Wormer, H., Ickstadt, K.: Towards a systematic screening tool for quality assurance and semiautomatic fraud detection for images in the life sciences. Sci. Eng. Ethics 23(4), 1113–1128 (2017)

    Google Scholar 

  • Kudo, S., Nitadori, K., Ina, T., Imamura, T.: Implementation and numerical techniques for one eflop/s hpl-ai benchmark on fugaku. In: 2020 IEEE/ACM 11th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA), IEEE, pp. 69–76 (2020)

  • Kumar, S., Huang, C., Zheng, G., Bohm, E., Bhatele, A., Phillips, J.C., Yu, H., Kalé, L.V.: Scalable molecular dynamics with namd on the ibm blue gene/l system. J. Res. Dev. 52(1.2), 177–188 (2008)

    Google Scholar 

  • Kurth, T., Treichler, S., Romero, J., Mudigonda, M., Luehr, N., Phillips, E., Mahesh, A., Matheson, M., Deslippe, J., Fatica, M., et al.: Exascale deep learning for climate analytics. In: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE, pp. 649–660 (2018)

  • Li, K., Shang, H., Zhang, Y., Li, S., Wu, B., Wang, D., Zhang, L., Li, F., Chen, D., Wei, Z.: Openkmc: a kmc design for hundred-billion-atom simulation using millions of cores on sunway taihulight. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–16 (2019)

  • Li, Z., Liu, G., Jiang, C.: Deep representation learning with full center loss for credit card fraud detection. IEEE Trans. Comput. Soc. Syst. 7(2), 569–579 (2020)

    Google Scholar 

  • Lin, H., Tang, X., Yu, B., Zhuo, Y., Chen, W., Zhai, J., Yin, W., Zheng, W.: Scalable graph traversal on sunway taihulight with ten million cores. In: 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS), IEEE, pp. 635–645 (2017)

  • Liu, Z., Chu, X., Lv, X., Meng, H., Shi, S., Han, W., Xu, J., Fu, H., Yang, G.: Sunwaylb: Enabling extreme-scale lattice boltzmann method based computing fluid dynamics simulations on sunway taihulight. In: 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), IEEE, pp. 557–566 (2019)

  • Lu, Y., Qian, D., Fu, H., Chen, W.: Will supercomputers be super-data and super-ai machines? Commun ACM 61(11), 82–87 (2018). https://doi.org/10.1145/3239556

    Article  Google Scholar 

  • Lu, Y.T., Cheng, P., Chen, Z.G.: Design and implementation of the tianhe-2 data storage and management system. J. Comput. Sci. Technol. 35(1), 27–46 (2020)

    Google Scholar 

  • Luo, H., Paal, S.G.: Advancing post-earthquake structural evaluations via sequential regression-based predictive mean matching for enhanced forecasting in the context of missing data. Adv. Eng. Inform. 47, 101202 (2021)

    Google Scholar 

  • Luo, L., Straatsma, T.P., Suarez, L.A., Broer, R., Bykov, D., D’Azevedo, E.F., Faraji, S.S., Gottiparthi, K.C., De Graaf, C., Harris, J.A., et al.: Pre-exascale accelerated application development: The ornl summit experience. IBM J. Res. Dev. 64(3/4), 11–1 (2020)

    Google Scholar 

  • Lv, G., Li, M., An, H., Lin, H., Chen, J., Han, W., Xiao, Q., Wang, F., Lin, R.: Distributed deep learning system for cancerous region detection on sunway taihulight. CCF Trans. High Performance Comput. 2(4), 348–361 (2020)

    Google Scholar 

  • Mapar, J., Holtermann, K., Legary, J., Mahrous, K., Guzman, K., Heath, Z., John, C.J., Mier, S.A., Mueller, S., Pancerella, C.M., et al.: The role of integrated modeling and simulation in disaster preparedness and emergency preparedness and response: the summit platform. In: 2012 IEEE Conference on Technologies for Homeland Security (HST), IEEE, pp. 117–122 (2012)

  • Massonnet, F., Bellprat, O., Guemas, V., Doblas-Reyes, F.J.: Using climate models to estimate the quality of global observational data sets. Science 354(6311), 452–455 (2016)

    Google Scholar 

  • Mazzucco, W., Pastorino, R., Lagerberg, T., Colotto, M., d’Andrea, E., Marotta, C., Marzuillo, C., Villari, P., Federici, A., Ricciardi, W., et al.: Current state of genomic policies in healthcare among eu member states: results of a survey of chief medical officers. Euro. J. Public Health 27(5), 931–937 (2017)

    Google Scholar 

  • Mehmood, Y., Ahmad, F., Yaqoob, I., Adnane, A., Imran, M., Guizani, S.: Internet-of-things-based smart cities: Recent advances and challenges. IEEE Commun. Mag. 55(9), 16–24 (2017)

    Google Scholar 

  • Middleton, A.M.: Data intensive supercomputing solutions. In: Big Data Technologies and Applications, Springer, pp. 257–306 (2016)

  • Minson, S.E., Meier, M.A., Baltay, A.S., Hanks, T.C., Cochran, E.S.: The limits of earthquake early warning: timeliness of ground motion estimates. Sci. Adv. 4(3), eaaq0504 (2018)

    Google Scholar 

  • Musser, G.: One of quantum physics’ greatest paradoxes may have lost its leading explanation. Science (2020)

  • Nakao, M., Ueno, K., Fujisawa, K., Kodama, Y., Sato, M.: Performance evaluation of supercomputer fugaku using breadth-first search benchmark in graph500. In: 2020 IEEE International Conference on Cluster Computing (CLUSTER), IEEE, pp. 408–409 (2020)

  • Odajima, T., Kodama, Y., Tsuji, M., Matsuda, M., Maruyama, Y., Sato, M.: Preliminary performance evaluation of the fujitsu a64fx using hpc applications. In: 2020 IEEE International Conference on Cluster Computing (CLUSTER), IEEE, pp. 523–530 (2020)

  • Orhan, A.E.: Robustness properties of facebook’s resnext wsl models. arXiv preprint arXiv:1907.07640 (2019)

  • Puertas-Martín, S., Banegas-Luna, A.J., Paredes-Ramos, M., Redondo, J.L., Ortigosa, P.M., Brovarets’, O.O., Pérez-Sánchez, H.: Is high performance computing a requirement for novel drug discovery and how will this impact academic efforts? Expert Opin. Drug Discovery 15(9), 981–985 (2020)

    Google Scholar 

  • Rajak, R.: A comparative study: Taxonomy of high performance computing (hpc). Int. J. Electr. Comput. Eng. 8(5), 3386 (2018)

    Google Scholar 

  • Sapountzi, A., Psannis, K.E.: Social networking data analysis tools & challenges. Future Generat. Comput. Syst. 86, 893–913 (2018)

    Google Scholar 

  • Schmidt, B., Hildebrandt, A.: Next-generation sequencing: big data meets high performance computing. Drug Discovery Today 22(4), 712–717 (2017)

    Google Scholar 

  • Seal, S.K., Lim, S.H., Wang, D., Hinkle, J., Lunga, D., Tsaris, A.: Toward large-scale image segmentation on summit. In: 49th International Conference on Parallel Processing-ICPP, pp. 1–11 (2020)

  • Sejdic, E., Malandraki, G.A., Coyle, J.L.: Computational deglutition: Using signal-and image-processing methods to understand swallowing and associated disorders [life sciences]. IEEE Signal Process. Mag. 36(1), 138–146 (2018)

    Google Scholar 

  • Shimizu, T.: Supercomputer fugaku: Co-designed with application developers/researchers. In: 2020 IEEE Asian Solid-State Circuits Conference (A-SSCC), IEEE, pp. 1–4 (2020)

  • Shipilova, E., Barret, M., Bloch, M., Boelle, J.L., Collette, J.L.: Simultaneous seismic sources separation based on matrioshka orthogonal matching pursuit, application in oil and gas exploration. IEEE Trans. Geosci. Remote Sens. 58(7), 4529–4546 (2020)

    Google Scholar 

  • Shuman, C.A., Steffen, K., Box, J.E., Stearns, C.R.: A dozen years of temperature observations at the summit: central greenland automatic weather stations 1987–99. J. Appl. Meteorol. 40(4), 741–752 (2001)

    Google Scholar 

  • Stavrinides, G.L., Karatza, H.D.: The impact of data locality on the performance of a saas cloud with real-time data-intensive applications. In: 2017 IEEE/ACM 21st International Symposium on Distributed Simulation and Real Time Applications (DS-RT), IEEE, pp. 1–8 (2017)

  • Tang, B., Chen, Z., Hefferman, G., Pei, S., Wei, T., He, H., Yang, Q.: Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Trans. Ind. Inform. 13(5), 2140–2150 (2017)

    Google Scholar 

  • Torbicki, M.: Longtime prediction of climate-weather change influence on critical infrastructure safety and resilience. In: 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE, pp. 996–1000 (2018)

  • Usman, S., Mehmood, R., Katib, I.: Big data and hpc convergence for smart infrastructures: a review and proposed architecture. Smart Infrastructure and Applications, pp. 561–586 (2020)

  • Wang, A., Zhang, A., Chan, E.H., Shi, W., Zhou, X., Liu, Z.: A review of human mobility research based on big data and its implication for smart city development. ISPRS Int. J. Geo-Inform. 10(1), 13 (2021)

    Google Scholar 

  • Wang, D., Yuan, F.: High-performance computing for earth system modeling. High Performance Computing for Geospatial Applications, pp. 175–184 (2020)

  • Wang, R., Tobar, R., Dolensky, M., An, T., Wicenec, A., Wu, C., Dulwich, F., Podhorszki, N., Anantharaj, V., Suchyta, E., et al.: Processing full-scale square kilometre array data on the summit supercomputer. In: SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE, pp. 1–12 (2020)

  • Womble, D.E., Shankar, M., Joubert, W., Johnston, J.T., Wells, J.C., Nichols, J.A.: Early experiences on summit: Data analytics and ai applications. IBM Journal of Research and Development 63(6), 2–1 (2019)

    Google Scholar 

  • Yu, Z., Zhu, K., Hattori, K., Chi, C., Fan, M., He, X.: Borehole strain observations based on a state-space model and apne analysis associated with the 2013 lushan earthquake. IEEE Access 9, 12167–12179 (2021)

    Google Scholar 

  • Zhang, J., Zhou, C., Wang, Y., Ju, L., Du, Q., Chi, X., Xu, D., Chen, D., Liu, Y., Liu, Z.: Extreme-scale phase field simulations of coarsening dynamics on the sunway taihulight supercomputer. In: SC’16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE, pp. 34–45 (2016)

  • Zhang, K., Su, H., Zhang, P., Dou, Y.: Optimization and performance modeling of stencil computations on arm architectures. In: 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), IEEE, pp. 113–121 (2020)

  • Zhang, P., Yin, D., Atkinson, P.M.: Future extreme climate prediction in western jilin province based on statistical downscaling model. In: IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, pp. 9886–9889 (2019a)

  • Zhang, X., Wang, Y., Wang, Q., Zhao, X.: A new approach to double i/o performance for ceph distributed file system in cloud computing. In: 2019 2nd International Conference on Data Intelligence and Security (ICDIS), IEEE, pp. 68–75 (2019b)

  • Zhang, Y., Zhu, Z., Cui, H., Dong, X., Chen, H.: Small files storing and computing optimization in hadoop parallel rendering. Concurrency Comput. 29(20), e3847 (2017)

    Google Scholar 

  • Zhong, X., Yang, H., Luan, Z., Gan, L., Yang, G., Qian, D.: swtensor: accelerating tensor decomposition on sunway architecture. CCF Trans. High Performance Comput. 1(3), 161–176 (2019). https://doi.org/10.1007/s42514-019-00017-5

    Article  Google Scholar 

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  1. School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, 710062, China

    Jia Wei, Longxiang Wang, Yujia Lei, Yuqi Qu, Qiyu Jiang, Xiaoshe Dong, Weiguo Wu, Qiang Wang & Xingjun Zhang

  2. Huawei technologies Co. Ltd., ShenZhen, 518129, China

    Mo Chen, Pei Ren & Kaili Zhang

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  1. Jia Wei
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Wei, J., Chen, M., Wang, L. et al. Status, challenges and trends of data-intensive supercomputing. CCF Trans. HPC 4, 211–230 (2022). https://doi.org/10.1007/s42514-022-00109-9

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