Year: 2024
Type: conference-paper
Abstract: In this work we consider scheduling strategies to deal with disaggregated memory for HPC systems. Disaggregated memory is an implementation of storage management that provides flexibility by giving the option to allocate storage based on system-defined parameters. In this case, we consider a memory hierarchy that allows to partition the memory resources arbitrarily amongst several nodes depending on the need. This memory can be dynamically reconfigured at a cost. We provide algorithms that pre-allocate or reconfigure dynamically the disaggregated memory based on estimated needs. We provide theoretical performance results for these algorithms. An important contribution of our work is that it shows that the system can design allocation algorithms even if user memory estimates are not accurate, and for dynamic memory patterns. These algorithms rely on statistical behavior of applications. We observe the impact on the performance of parameters of interest such as the reconfiguration cost.
Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Institut de Recherche en Informatique et Systèmes Aléatoires, KERDATA: Stockage et traitement de données sur le continuum Edge-Cloud-HPC, Université de Rennes, Institut polytechnique de Grenoble, Laboratoire d'Informatique de Grenoble, DATAMOVE: Mouvements de données pour le calcul haute performance, Université Grenoble Alpes
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4405148226