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Towards Dynamic Reconfigurable Load-balancing for Hybrid Desktop Platforms

Binotto, Alécio P. D. and Pereira, Carlos Eduardo and Fellner, Dieter W. (2010):
Towards Dynamic Reconfigurable Load-balancing for Hybrid Desktop Platforms.
IEEE Computer Society Press, New York, In: 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, p. 4, DOI: 10.1109/IPDPSW.2010.5470804,
[Conference or Workshop Item]

Abstract

High-performance platforms are required by applications that use massive calculations. Actually, desktop accelerators (like the GPUs) form a powerful heterogeneous platform in conjunction with multi-core CPUs. To improve application performance on these hybrid platforms, load-balancing plays an important role to distribute workload. However, such scheduling problem faces challenges since the cost of a task at a Processing Unit (PU) is non-deterministic and depends on parameters that cannot be known a priori, like input data, online creation of tasks, scenario changing, etc. Therefore, self-adaptive computing is a potential paradigm as it can provide flexibility to explore computational resources and improve performance on different execution scenarios. This paper presents an ongoing PhD research focused on a dynamic and reconfigurable scheduling strategy based on timing profiling for desktop accelerators. Preliminary results analyze the performance of solvers for SLEs (Systems of Linear Equations) over a hybrid CPU and multi-GPU platform applied to a CFD (Computational Fluid Dynamics) application. The decision of choosing the best solver as well as its scheduling must be performed dynamically considering online parameters in order to achieve a better application performance.

Item Type: Conference or Workshop Item
Erschienen: 2010
Creators: Binotto, Alécio P. D. and Pereira, Carlos Eduardo and Fellner, Dieter W.
Title: Towards Dynamic Reconfigurable Load-balancing for Hybrid Desktop Platforms
Language: English
Abstract:

High-performance platforms are required by applications that use massive calculations. Actually, desktop accelerators (like the GPUs) form a powerful heterogeneous platform in conjunction with multi-core CPUs. To improve application performance on these hybrid platforms, load-balancing plays an important role to distribute workload. However, such scheduling problem faces challenges since the cost of a task at a Processing Unit (PU) is non-deterministic and depends on parameters that cannot be known a priori, like input data, online creation of tasks, scenario changing, etc. Therefore, self-adaptive computing is a potential paradigm as it can provide flexibility to explore computational resources and improve performance on different execution scenarios. This paper presents an ongoing PhD research focused on a dynamic and reconfigurable scheduling strategy based on timing profiling for desktop accelerators. Preliminary results analyze the performance of solvers for SLEs (Systems of Linear Equations) over a hybrid CPU and multi-GPU platform applied to a CFD (Computational Fluid Dynamics) application. The decision of choosing the best solver as well as its scheduling must be performed dynamically considering online parameters in order to achieve a better application performance.

Publisher: IEEE Computer Society Press, New York
Uncontrolled Keywords: Forschungsgruppe Semantic Models, Immersive Systems (SMIS), Graphics processors, Parallel processing, Load balancing
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1109/IPDPSW.2010.5470804
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