2026-04-13T02:29:24Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1866532026-01-27T04:28:42Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Kavanagh, Richard author Djemame, Karim author Ejarque Artigas, Jorge author Badia Sala, Rosa Maria author Garcia Perez, David author 2020-01 Hardware in High Performance Computing environments in recent years have increasingly become more heterogeneous in order to improve computational performance. An additional aspect of such systems is the management of power and energy consumption. The increase in heterogeneity requires middleware and programming model abstractions to eliminate additional complexities that it brings, while also offering opportunities such as improved power management. In this paper, we explore application level self-adaptation including aspects such as automated configuration and deployment of applications to different heterogeneous infrastructure and for their redeployment. This therefore not only mitigates complexities associated with heterogeneous devices but aims to take advantage of the heterogeneity. The overall result of this paper is a self-adaptive framework that manages application Quality of Service (QoS) at runtime, which includes the automatic migration of applications between different accelerated infrastructures. Discussion covers when this migration is appropriate and quantifies the likely benefits. The authors would like to thank the European Commission for supporting this work under the Horizon 2020 Research and Innovation program under contract 687584 (TANGO project). Peer Reviewed Postprint (author's final draft) Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors High performance computing -- Energy consumption Parallel processing (Electronic computers) Quality of service (Computer networks) Self-adaptation Energy modelling Programming model Heterogeneous hardware architectures Application deployment Càlcul intensiu (Informàtica) -- Consum d'energia Processament en paral·lel (Ordinadors) Energy-aware self-adaptation for application execution on heterogeneous parallel architectures