This article is centred on a mathematical weather forecasting model that must run regularly (i.e. 24/7) on an HPC system. Depending on the environmental conditions, each execution of the model may have a different deadline and a different accuracy requirement. In order to minimize power consumption and heat, we minimize resource allocation as far as the deadlines allow, thus evenly spreading resource usage over time while nonetheless complying with the deadlines. Our work relies on a run-time resource manager that adapts resource allocation to the runtimevariable performance demand of applications. The resource assignment is temperature-aware: the application is dynamically migrated on the coolest cores, and this has a positive impact on the system reliability.

Just-in-time execution through on-demand resource allocation in HPC systems

Libutti, S.;Massari, G.;Fornaciari, W.
2017-01-01

Abstract

This article is centred on a mathematical weather forecasting model that must run regularly (i.e. 24/7) on an HPC system. Depending on the environmental conditions, each execution of the model may have a different deadline and a different accuracy requirement. In order to minimize power consumption and heat, we minimize resource allocation as far as the deadlines allow, thus evenly spreading resource usage over time while nonetheless complying with the deadlines. Our work relies on a run-time resource manager that adapts resource allocation to the runtimevariable performance demand of applications. The resource assignment is temperature-aware: the application is dynamically migrated on the coolest cores, and this has a positive impact on the system reliability.
2017
ACM International Conference Proceeding Series
9781450352840
HPC; Monte-Carlo simulation; Parallel execution; Reliability; Runtime; Human-Computer Interaction; Computer Networks and Communications; 1707; Software
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1072217
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