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Hercules: Scalable and Network Portable In-Memory Ad-Hoc File System for Data-Centric and High-Performance Applications

Contributo in Atti di convegno
Data di Pubblicazione:
2023
Abstract:
The growing demands for data processing by new data-intensive applications are putting pressure on the performance and capacity of HPC storage systems. The advancement in storage technologies, such as NVMe and persistent memory, are aimed at meeting these demands. However, relying solely on ultra-fast storage devices is not cost-effective, leading to the need for multi-tier storage hierarchies to move data based on its usage. To address this issue, ad-hoc file systems have been proposed as a solution. They utilise the available storage of compute nodes, such as memory and persistent storage, to create a temporary file system that adapts to the application behaviour in the HPC environment. This work presents the design, implementation, and evaluation of a distributed ad-hoc in-memory storage system (Hercules), highlighting the new communication model included in Hercules. This communication model takes advantage of the Unified Communication X framework (UCX). This solution leverages the capabilities of RDMA protocols, including Infiniband, Onmipath, shared memory, and zero-copy transfers. The preliminary evaluation results show excellent network utilisation compared with other existing technologies.
Tipologia CRIS:
04A-Conference paper in volume
Keywords:
Data intensive; HPC; In-memory storage
Elenco autori:
Garcia-Blas J.; Sanchez-Gallegos G.; Petre C.; Martinelli A.R.; Aldinucci M.; Carretero J.
Autori di Ateneo:
ALDINUCCI Marco
Link alla scheda completa:
https://iris.unito.it/handle/2318/1946992
Titolo del libro:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pubblicato in:
LECTURE NOTES IN COMPUTER SCIENCE
Journal
LECTURE NOTES IN COMPUTER SCIENCE
Series
Progetto:
Third Party CINI - "ADMIRE - Adaptive multi-tier intelligent data manager for Exascale" - Call H2020-JTI-EuroHPC-2019-1 - Grant Agreement n. 956748 - CUP F69J21003450007
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