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Platform Calibration for Load Balancing of Large Simulations: TLM Case

Cristian Ruiz 1, * Mihai Alexandru 2, 3 Olivier Richard 1 Thierry Monteil 3, 2 Hervé Aubert 2, 3 
* Corresponding author
1 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
2 LAAS-SARA - Équipe Services et Architectures pour Réseaux Avancés
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : The heterogeneous nature of distributed platforms such as computational Grids is one of the main barriers to effectively deploy tightly-coupled applications. For those applications , one common problem that appears due to the hardware heterogeneity is the load imbalance which slows down the application to the pace of the slower processor. One solution is to distribute the load adequately taking into account hardware capacities. To do so, an estimation of the hardware capacities for running the application has to be obtained. In this paper, we present a static load balancing for iterative tightly-coupled applications based on a profile prediction model. This technique is presented as a successful example of the interaction between experiment management tools and parallel applications. The experiment management tool Expo is used that enabled to: (1) provide a general, lightweight and descriptive way to capture the tuning and deployment of a parallel application in a computing infrastructure, (2) perform the tuning of the application efficiently in terms of human effort and resources needed. This paper reports the costs for carrying out the tuning of a large electromagnetic simulation based on TLM for the platform Grid'5000 and the improvements obtained on the total execution time of the application.
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Submitted on : Thursday, November 12, 2015 - 10:45:31 PM
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Cristian Ruiz, Mihai Alexandru, Olivier Richard, Thierry Monteil, Hervé Aubert. Platform Calibration for Load Balancing of Large Simulations: TLM Case. IEEE/ACM International Symposium on Cluster, Cloud and grid Computing ( IEEE/ACM CCGrid ), May 2014, Chicago, United States. pp.465-472, ⟨10.1109/CCGrid.2014.26⟩. ⟨hal-01228344⟩



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