Towards dynamic reconfigurable load-balancing for hybrid desktop platforms

Alecio P.D. Binotto, Carlos E. Pereira, Dieter W. Fellner
Inf. Inst., UFRGS – Fed. Univ. of Rio Grande do Sul, Porto Alegre, Brazil
IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010


   title={Towards dynamic reconfigurable load-balancing for hybrid desktop platforms},

   author={Binotto, A.P.D. and Pereira, C.E. and Fellner, D.W.},

   booktitle={Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on},





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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.
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