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Communication Dans Un Congrès Année : 2023

GrIOt: Graph-based Modeling of HPC Application I/O Call Stacks for Predictive Prefetch

Résumé

Modern High Performance Computing (HPC) storage systems use heterogeneous storage technologies organized in tiers to find a compromise between capacity, performance, and cost. In these systems, prefetching is a common technique used to move the right data at the right moment from a slow to a fast tier to improve the overall performance while using the costly high-performance tier only when needed. Effective prefetching requires precise knowledge of the application I/O patterns. This knowledge can be extracted through the source code, I/O tracing tools or I/O functions call stacks. State-of-the-art solutions based on the latter approach mainly focus on applications with regular I/O profiles to avoid scalability issues due to the grammar-based techniques used. In this paper, we present an approach based on I/O call stacks that models I/O patterns for both regular and irregular applications, thanks to the use of directed graphs. We present different models for prefetching. Our models were used to predict the next I/O call stack on two real HPC applications and one synthetic workload with an accuracy of up to 98%, while keeping a low overheadt.
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Dates et versions

hal-04290790 , version 1 (17-11-2023)

Identifiants

Citer

Louis-Marie Nicolas, Salim Mimouni, Philippe Couvée, Jalil Boukhobza. GrIOt: Graph-based Modeling of HPC Application I/O Call Stacks for Predictive Prefetch. SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis - 8th International Parallel Data Systems Workshop, Nov 2023, Denver CO USA, France. pp.1195-1201, ⟨10.1145/3624062.3624189⟩. ⟨hal-04290790⟩
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