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In this paper we would like to share our experience for transforming a parallel code for a Computational Fluid Dynamics (CFD) problem into a parallel version for the RedisDG workflow engine. This system is able to capture heterogeneous and highly dynamic environments, thanks to opportunistic scheduling strategies. We show how to move to the field of HPC as a Service in order to use heterogeneous platforms. We mainly explain, through the CFD use case, how to transform the parallel code and we exhibit challenges to unfold the task graph dynamically in order to improve the overall performance (in a broad sense) of the workflow engine. We discuss in particular of the impact on the workflow engine of such dynamic feature. This paper states that new models for High Performance Computing are possible, under the condition we revisit our mind in the direction of the potential of new paradigms such as cloud, edge computing.
This paper aims to create a transition path from file-based IO to streaming-based workflows for scientific applications in an HPC environment. By using the openPMP-api, traditional workflows limited by filesystem bottlenecks can be overcome and flexi
Hardware-aware design and optimization is crucial in exploiting emerging architectures for PDE-based computational fluid dynamics applications. In this work, we study optimizations aimed at acceleration of OpenFOAM-based applications on emerging hybr
Data centres that use consumer-grade disks drives and distributed peer-to-peer systems are unreliable environments to archive data without enough redundancy. Most redundancy schemes are not completely effective for providing high availability, durabi
Traditional heterogeneous parallel algorithms, designed for heterogeneous clusters of workstations, are based on the assumption that the absolute speed of the processors does not depend on the size of the computational task. This assumption proved in
RECIPE (REliable power and time-ConstraInts-aware Predictive management of heterogeneous Exascale systems) is a recently started project funded within the H2020 FETHPC programme, which is expressly targeted at exploring new High-Performance Computing