Dynamic Self-Rescheduling of Tasks over a Heterogeneous Platform

A.P.D. Binotto, E.P. Freitas, M. Gotz, C.E. Pereira, A. Stork, T. Larsson
Fraunhofer IGD, Tech. Universitdt Darmstadt, Darmstadt
International Conference on Reconfigurable Computing and FPGAs, 2008. ReConFig ’08


   title={Dynamic Self-Rescheduling of Tasks over a Heterogeneous Platform},

   author={Binotto, A.P.D. and Freitas, E.P. and G{\"o}tz, M. and Pereira, C.E. and Stork, A. and Larsson, T.},

   booktitle={2008 International Conference on Reconfigurable Computing and FPGAs},





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Modern applications require powerful high-performance platforms to deal with many different algorithms that make use of massive calculations. At the same time, low-cost and high-performance specific hardware (e.g., GPU, PPU) are rising and the CPUs turned to multiple cores, characterizing together an interesting and powerful heterogeneous execution platform. Therefore, self-adaptive computing is a potential paradigm for those scenarios as it can provide flexibility to explore the computational resources on heterogeneous cluster attached to a high-performance computer system platform. As the first step towards a run-time reschedule load-balancing framework targeting that kind of platform, application time requirements and its crosscutting behavior play an important role for task allocation decisions. This paper presents a strategy for self-reallocation of specific tasks, including dynamic created ones, using aspect-oriented paradigms to address non-functional application timing constraints in the design phase. Additionally, as a case study, a special attention on radar image processing will be given in the context of a surveillance system based on unmanned aerial vehicles (UAV).
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