{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T15:58:54Z","timestamp":1789142334257,"version":"build-2803163510"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T00:00:00Z","timestamp":1732924800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T00:00:00Z","timestamp":1732924800000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s00521-024-10489-4","type":"journal-article","created":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T14:22:16Z","timestamp":1732976536000},"page":"1757-1769","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Distributed data processing and task scheduling based on GPU parallel computing"],"prefix":"10.1007","volume":"37","author":[{"given":"Jun","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,30]]},"reference":[{"key":"10489_CR1","doi-asserted-by":"crossref","first-page":"59968","DOI":"10.1109\/ACCESS.2021.3073955","volume":"9","author":"L Wan","year":"2021","unstructured":"Wan L, Zheng W, Yuan X (2021) Efficient inter-device task scheduling schemes for multi-device co-processing of data-parallel kernels on heterogeneous systems. IEEE Access 9:59968\u201359978","journal-title":"IEEE Access"},{"issue":"4","key":"10489_CR2","first-page":"1041","volume":"12","author":"IM Ibrahim","year":"2021","unstructured":"Ibrahim IM (2021) Task scheduling algorithms in cloud computing: a review. Turk J Comput Math Edu (TURCOMAT) 12(4):1041\u20131053","journal-title":"Turk J Comput Math Edu (TURCOMAT)"},{"key":"10489_CR3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.future.2021.05.012","volume":"124","author":"W Shu","year":"2021","unstructured":"Shu W, Cai K, Xiong NN (2021) Research on strong agile response task scheduling optimization enhancement with optimal resource usage in green cloud computing. Future Gener Comput Syst 124:12\u201320","journal-title":"Future Gener Comput Syst"},{"issue":"4","key":"10489_CR4","first-page":"28","volume":"13","author":"Z Jalalian","year":"2021","unstructured":"Jalalian Z, Sharifi M (2021) A survey on task scheduling algorithms in cloud computing for fast big data processing. Int J Inf Commun Technol Res 13(4):28\u201335","journal-title":"Int J Inf Commun Technol Res"},{"issue":"6","key":"10489_CR5","doi-asserted-by":"crossref","first-page":"5960","DOI":"10.1007\/s11227-020-03506-5","volume":"77","author":"J Li","year":"2021","unstructured":"Li J, Zhang X, Han L, Ji Z, Dong X, Hu C (2021) OKCM: improving parallel task scheduling in high-performance computing systems using online learning. J Supercomput 77(6):5960\u20135983","journal-title":"J Supercomput"},{"issue":"1","key":"10489_CR6","doi-asserted-by":"crossref","first-page":"108","DOI":"10.3390\/s22010108","volume":"22","author":"A Ali","year":"2021","unstructured":"Ali A, Iqbal MM, Jamil H, Akbar H, Muthanna A, Ammi M, Althobaiti MM (2021) Multilevel central trust management approach for task scheduling on IoT-based mobile cloud computing. Sensors 22(1):108","journal-title":"Sensors"},{"issue":"7","key":"10489_CR7","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1007\/s00607-021-00935-9","volume":"103","author":"R Madhura","year":"2021","unstructured":"Madhura R, Elizabeth BL, Uthariaraj VR (2021) An improved list-based task scheduling algorithm for fog computing environment. Computing 103(7):1353\u20131389","journal-title":"Computing"},{"key":"10489_CR8","doi-asserted-by":"crossref","first-page":"100841","DOI":"10.1016\/j.swevo.2021.100841","volume":"62","author":"EH Houssein","year":"2021","unstructured":"Houssein EH, Gad AG, Wazery YM, Suganthan PN (2021) Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends. Swarm Evol Comput 62:100841","journal-title":"Swarm Evol Comput"},{"issue":"8","key":"10489_CR9","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1109\/JPROC.2021.3087029","volume":"109","author":"Z Wu","year":"2021","unstructured":"Wu Z, Sun J, Zhang Y, Wei Z, Chanussot J (2021) Recent developments in parallel and distributed computing for remotely sensed big data processing. Proc IEEE 109(8):1282\u20131305","journal-title":"Proc IEEE"},{"issue":"6","key":"10489_CR10","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MNET.201.2100179","volume":"35","author":"X Wang","year":"2021","unstructured":"Wang X, Wang S, Wang Y, Ning Z, Guo L (2021) Distributed task scheduling for wireless powered mobile edge computing: a federated-learning-enabled framework. IEEE Netw 35(6):27\u201333","journal-title":"IEEE Netw"},{"key":"10489_CR11","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.procs.2021.03.014","volume":"184","author":"R Firouzi","year":"2021","unstructured":"Firouzi R, Rahmani R, Kanter T (2021) Distributed-reasoning for task scheduling through distributed internet of things controller. Procedia Comput Sci 184:24\u201332","journal-title":"Procedia Comput Sci"},{"key":"10489_CR12","doi-asserted-by":"crossref","unstructured":"Cui Y, Zhang D, Zhang T, Yang P, Zhu H (2021) A new approach on task offloading scheduling for application of mobile edge computing. In: 2021 IEEE wireless communications and networking conference (WCNC). IEEE, pp 1\u20136","DOI":"10.1109\/WCNC49053.2021.9417286"},{"issue":"3","key":"10489_CR13","doi-asserted-by":"crossref","first-page":"779","DOI":"10.3390\/s21030779","volume":"21","author":"S Chen","year":"2021","unstructured":"Chen S, Li Q, Zhou M, Abusorrah A (2021) Recent advances in collaborative scheduling of computing tasks in an edge computing paradigm. Sensors 21(3):779","journal-title":"Sensors"},{"key":"10489_CR14","doi-asserted-by":"crossref","unstructured":"Wang Y, Tang S, Zhu X, Xie Y (2021) A novel multitask scheduling and distributed collaborative computing method of edge nodes in the internet of things. Wirel Commun Mob Comput","DOI":"10.1155\/2021\/1548142"},{"key":"10489_CR15","doi-asserted-by":"crossref","first-page":"104017","DOI":"10.1016\/j.micpro.2021.104017","volume":"83","author":"H Huang","year":"2021","unstructured":"Huang H, Xu X, Tang C (2021) Design of parallel computing system for embedded network distributed load tasks. Microprocess Microsyst 83:104017","journal-title":"Microprocess Microsyst"},{"key":"10489_CR16","doi-asserted-by":"crossref","unstructured":"Wang Y, Yang RR, Xu YX, Li X, Shi JL (2021) Research on multi-agent task optimization and scheduling based on improved ant colony algorithm. In: IOP conference series: materials science and engineering, Vol 1043(3). IOP Publishing, p 032007","DOI":"10.1088\/1757-899X\/1043\/3\/032007"},{"issue":"1","key":"10489_CR17","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s00607-014-0392-y","volume":"98","author":"Y Ma","year":"2016","unstructured":"Ma Y, Chen L, Liu P, Lu K (2016) Parallel programing templates for remote sensing image processing on GPU architectures: design and implementation. Computing 98(1):7\u201333","journal-title":"Computing"},{"issue":"11","key":"10489_CR18","doi-asserted-by":"crossref","first-page":"13861","DOI":"10.1109\/TVT.2020.3029864","volume":"69","author":"Q Qi","year":"2020","unstructured":"Qi Q, Zhang L, Wang J, Sun H, Zhuang Z, Liao J, Yu FR (2020) Scalable parallel task scheduling for autonomous driving using multitask deep reinforcement learning. IEEE Trans Veh Technol 69(11):13861\u201313874","journal-title":"IEEE Trans Veh Technol"},{"key":"10489_CR19","unstructured":"Cheng, J. R., & Gen, M. (2020). Parallel genetic algorithms with GPU computing. In: Industry 4.0-impact on intelligent logistics and manufacturing. IntechOpen"},{"key":"10489_CR20","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/978-981-15-3914-5_13","volume-title":"Intelligent systems, technologies and applications: proceedings of fifth ISTA 2019, India","author":"L Yuan","year":"2020","unstructured":"Yuan L, Dong Y, Li Y, Zhang R, Xie H (2020) A task parallel programming framework based on heterogeneous computing platforms. In: Thampi SM, Trajkovic L, Mitra S, Nagabhushan P, El-Alfy E-SM, Bojkovic Z, Mishra D (eds) Intelligent systems, technologies and applications: proceedings of fifth ISTA 2019, India. Springer, Singapore, pp 169\u2013184"},{"issue":"4","key":"10489_CR21","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.1109\/TNET.2020.2987845","volume":"28","author":"K Xie","year":"2020","unstructured":"Xie K, Chen Y, Wang X, Xie G, Cao J, Wen J, Yang G, Sun J (2020) Accurate and fast recovery of network monitoring data with GPU-accelerated tensor completion. IEEE\/ACM Trans Netw 28(4):1601\u20131614. https:\/\/doi.org\/10.1109\/TNET.2020.2987845","journal-title":"IEEE\/ACM Trans Netw"},{"issue":"9","key":"10489_CR22","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.3390\/en13092147","volume":"13","author":"DH Yoon","year":"2020","unstructured":"Yoon DH, Han Y (2020) Parallel power flow computation trends and applications: a review focusing on GPU. Energies 13(9):2147","journal-title":"Energies"},{"issue":"18","key":"10489_CR23","doi-asserted-by":"crossref","first-page":"e5823","DOI":"10.1002\/cpe.5823","volume":"33","author":"L Thamsen","year":"2021","unstructured":"Thamsen L, Beilharz J, Tran VT, Nedelkoski S, Kao O (2021) Mary, Hugo, and Hugo*: learning to schedule distributed data-parallel processing jobs on shared clusters. Concurr Comput Pract Exp 33(18):e5823","journal-title":"Concurr Comput Pract Exp"},{"key":"10489_CR24","unstructured":"Tang Z, Xiao Z, Yang L, He K, Li K (2021) A network load perception based task scheduler for parallel distributed data processing systems. IEEE Trans Cloud Comput"},{"key":"10489_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/8691646","volume":"2022","author":"M Ishaq","year":"2022","unstructured":"Ishaq M, Khan A, Su\u2019ud MM, Alam MM, Bangash JI, Khan A (2022) An improved strategy for task scheduling in the parallel computational alignment of multiple sequences. Comput Math Methods Med 2022:1\u201311. https:\/\/doi.org\/10.1155\/2022\/8691646","journal-title":"Comput Math Methods Med"},{"key":"10489_CR26","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.ins.2018.04.053","volume":"496","author":"Y Fang","year":"2019","unstructured":"Fang Y, Chen Q, Xiong N (2019) A multi-factor monitoring fault tolerance model based on a GPU cluster for big data processing. Inf Sci 496:300\u2013316","journal-title":"Inf Sci"},{"key":"10489_CR27","doi-asserted-by":"crossref","unstructured":"Zhang L, Qi Q, Wang J, Sun H, Liao J (2019) Multitask deep reinforcement learning for scalable parallel task scheduling. In: 2019 IEEE international conference on big data (big data). IEEE, pp 2992\u20133001","DOI":"10.1109\/BigData47090.2019.9006027"},{"issue":"4","key":"10489_CR28","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1007\/s11036-019-01356-x","volume":"25","author":"Q Tian","year":"2020","unstructured":"Tian Q, Li J, Xue D, Wu W, Wang J, Chen L, Wang J (2020) A hybrid task scheduling algorithm based on task clustering. Mob Netw Appl 25(4):1518\u20131527","journal-title":"Mob Netw Appl"},{"issue":"4","key":"10489_CR29","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1007\/s11227-018-2238-4","volume":"74","author":"P Thoman","year":"2018","unstructured":"Thoman P, Dichev K, Heller T, Iakymchuk R, Aguilar X, Hasanov K, Nikolopoulos DS (2018) A taxonomy of task-based parallel programming technologies for high-performance computing. J Supercomput 74(4):1422\u20131434","journal-title":"J Supercomput"},{"issue":"9","key":"10489_CR30","doi-asserted-by":"crossref","first-page":"8099","DOI":"10.1109\/JIOT.2020.2996784","volume":"7","author":"H Wu","year":"2020","unstructured":"Wu H, Zhang Z, Guan C, Wolter K, Xu M (2020) Collaborate edge and cloud computing with distributed deep learning for smart city internet of things. IEEE Internet Things J 7(9):8099\u20138110","journal-title":"IEEE Internet Things J"},{"issue":"1","key":"10489_CR31","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1186\/s13638-021-02022-1","volume":"2021","author":"H Huang","year":"2021","unstructured":"Huang H, Lu S, Wu Z, Wei Q (2021) An efficient authentication and key agreement protocol for IoT-enabled devices in distributed cloud computing architecture. EURASIP J Wirel Commun Netw 2021(1):150","journal-title":"EURASIP J Wirel Commun Netw"},{"key":"10489_CR32","unstructured":"Fan Y (2021) Job scheduling in high performance computing. arXiv preprint arXiv:2109.09269"},{"issue":"6","key":"10489_CR33","doi-asserted-by":"crossref","first-page":"2952","DOI":"10.3390\/s23062952","volume":"23","author":"AHA Al-Jumaili","year":"2023","unstructured":"Al-Jumaili AHA, Muniyandi RC, Hasan MK, Paw JKS, Singh MJ (2023) Big data analytics using cloud computing based frameworks for power management systems: status, constraints, and future recommendations. Sensors 23(6):2952","journal-title":"Sensors"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10489-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10489-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10489-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T22:00:51Z","timestamp":1738792851000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10489-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,30]]},"references-count":33,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10489"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10489-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,30]]},"assertion":[{"value":"3 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2024","order":3,"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 they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}