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Conference papers

Towards On-Demand I/O Forwarding in HPC Platforms

Abstract : I/O forwarding is an established and widely-adopted technique in HPC to reduce contention and improve I/O performance in the access to shared storage infrastructure. On such machines, this layer is often physically deployed on dedicated nodes, and their connection to the clients is static. Furthermore, the increasingly heterogeneous workloads entering HPC installations stress the I/O stack, requiring tuning and reconfiguration based on the applications' characteristics.t Nonetheless, it is not always feasible in a production system to explore the potential benefits of this layer under different configurations without impacting clients. In this paper, we investigate the effects of I/O forwarding on performance by considering the application's I/O access patterns and system characteristics. We aim to explore when forwarding is the best choice for an application, how many I/O nodes it would benefit from, and whether not using forwarding at all might be the correct decision. To gather performance metrics, explore, and understand the impact of forwarding I/O requests of different access patterns, we implemented FORGE, a lightweight I/O forwarding layer in user-space. Using FORGE, we evaluated the optimal forwarding configurations for several access patterns on MareNostrum 4 (Spain) and Santos Dumont (Brazil) supercomputers. Our results demonstrate that shifting the focus from a static system-wide deployment to an on-demand reconfigurable I/O forwarding layer dictated by application demands can improve I/O performance on future machines.
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Contributor : Francieli Zanon Boito Connect in order to contact the contributor
Submitted on : Tuesday, February 23, 2021 - 2:29:51 PM
Last modification on : Friday, January 21, 2022 - 3:10:12 AM
Long-term archiving on: : Monday, May 24, 2021 - 8:16:34 PM


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  • HAL Id : hal-03150024, version 1



Jean Luca Bez, Francieli Zanon Boito, Alberto Miranda, Ramon Nou, Toni Cortes, et al.. Towards On-Demand I/O Forwarding in HPC Platforms. PDSW 2020 : 5th IEEE/ACM International Parallel Data Systems Workshop, Nov 2020, Atlanta, Georgia / Virtual, United States. ⟨hal-03150024⟩



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