8856

Scheduling (ir)regular applications on heterogeneous platforms

Artur Miguel Matos Mariano
University of Minho, Braga, Portugal
University of Minho, 2012
@article{mariano2012scheduling,

   title={Scheduling (ir) regular applications on heterogeneous platforms},

   author={Mariano, A.M.M.},

   year={2012}

}

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Current computational platforms have become continuously more and more heterogeneous and parallel over the last years, as a consequence of incorporating accelerators whose architectures are parallel and different from the CPU. As a result, several frameworks were developed to aid to program these platforms mainly targeting better productivity ratios. In this context, GAMA framework is being developed by the research group involved in this work, targeting both regular and irregular algorithms to efficiently run in heterogeneous platforms. Scheduling is a key issue of GAMA-like frameworks. The state of the art solutions of scheduling on heterogeneous platforms are efficient for regular applications but lack adequate mechanisms for irregular ones. The scheduling of irregular applications is particularly complex due to the unpredictability and the differences on the execution time of their composing computational tasks. This dissertation work comprises the design and validation of a dynamic scheduler’s model and implementation, to simultaneously address regular and irregular algorithms. The devised scheduling mechanism is validated within the GAMA framework, when running relevant scientific algorithms, which include the SAXPY, the Fast Fourier Transform and two n-Body solvers. The proposed mechanism is validated regarding its efficiency in finding good scheduling decisions and the efficiency and scalability of GAMA, when using it. The results show that the model of the devised dynamic scheduler is capable of working in heterogeneous systems with high efficiency and finding good scheduling decisions in the general tested cases. It achieves not only the scheduling decision that represents the real capacity of the devices in the platform, but also enables GAMA to achieve more than 100% of efficiency as defined in [3], when running a relevant scientific irregular algorithm. Under the designed scheduling model, GAMA was also able to beat CPU and GPU efficient libraries of SAXPY, an important scientific algorithm. It was also proved GAMA’s scalability under the devised dynamic scheduler, which properly leveraged the platform computational resources, in trials with one central quad-core CPU-chip and two GPU accelerators.
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