Malleable and Adaptive Ad-Hoc File System for Data Intensive Workloads in HPC Applications
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-40843-4_5
Reference13 articles.
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2. Ahn, D.H., et al.: Flux: overcoming scheduling challenges for exascale workflows. Futur. Gener. Comput. Syst. 110, 202–213 (2020)
3. Cheriere, N., Dorier, M., Antoniu, G., Wild, S.M., Leyffer, S., Ross, R.: PufferScale: rescaling HPC data services for high energy physics applications. In: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), pp. 182–191. IEEE Computer Society, Los Alamitos (2020)
4. Chowdhury, F., et al.: I/O characterization and performance evaluation of BeeGFS for deep learning. In: Proceedings of the 48th International Conference on Parallel Processing, ICPP 2019. Association for Computing Machinery (2019)
5. Isaila, F., Carretero, J., Ross, R.: CLARISSE: a middleware for data-staging coordination and control on large-scale HPC platforms. In: 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 346–355 (2016)
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