most citedDMRlib: Easy-coding and Efficient Resource Management for Job Malleability

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cs.DC20264 cited

Leveraging Teaching on Demand: Approaching HPC to Undergrads

S. Catalán, R. Carratalá-Sáez, S. Iserte

High Performance Computing (HPC) is a highly demanded discipline in companies and institutions. However, as students and also afterwards as professors, we observed a lack of HPC re…

cs.DC20261 cited

Adaptation of AI-accelerated CFD Simulations to the IPU platform

P. Rosciszewski, A. Krzywaniak, S. Iserte +2

Intelligence Processing Units (IPU) have proven useful for many AI applications. In this paper, we evaluate them within the emerging field of \emph{AI for simulation}, where tradit…

cs.DC20266 cited

A Study on the Performance of Distributed Training of Data-driven CFD Simulations

Sergio Iserte, Alejandro González-Barberá, Paloma Barreda +1

Data-driven methods for computer simulations are blooming in many scientific areas. The traditional approach to simulating physical behaviors relies on solving partial differential…

cs.DC20263 cited

Towards the Democratization and Standardization of Dynamic Resources with MPI Spawning

Sergio Iserte, Iker Martín-Alvarez, Krzystof Rojek +3

This paper presents an efficient tool for managing dynamic resources in production high-performance computing (HPC) settings, focusing on flexibility, adaptability, and user-friend…

cs.DC202641 cited

DMRlib: Easy-coding and Efficient Resource Management for Job Malleability

Sergio Iserte, Rafael Mayo, Enrique S. Quintana-Ortí +1

Process malleability has proved to have a highly positive impact on the resource utilization and global productivity in data centers compared with the conventional static resource…