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Springer Nature Switzerland
Reference15 articles.
1. Antici, F., Yamamoto, K., Domke, J., Kiziltan, Z.: Augmenting ML-based predictive modelling with NLP to forecast a job’s power consumption. In: Proceedings of the Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis. The 1st International Workshop on the Environmental Sustainability of High-Performance Software. IEEE (2023, to appear). https://doi.org/10.1145/3624062.3624263
2. Banjongkan, A., Pongsena, W., Kerdprasop, N., et al.: A study of job failure prediction at job submit-state and job start-state in high-performance computing system: using decision tree algorithms. JAIT 12, 84–92 (2021)
3. Borghesi, A., et al.: M100 dataset (2023). https://gitlab.com/ecs-lab/exadata
4. Carpenter, P.M., et al.: ETP4HPC’s SRA 5 strategic research agenda for high-performance computing in Europe 2022: European HPC research priorities 2023–2027 (2022)
5. Chen, X., Lu, C.D., Pattabiraman, K.: Failure prediction of jobs in compute clouds: a Google cluster case study. In: 2014 IEEE ISSRE Workshops, pp. 341–346 (2014)