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Scheduling of Dense Linear Algebra Kernels on Heterogeneous Resources

Abstract : Due to massive computation power of accelerators such as GPU, Xeon phi, multicore machines equipped with accelerators are becoming popular in High Performance Computing (HPC). The added complexity led to the development of different task-based runtime systems, which allow computations to be expressed as graphs of tasks and rely on runtime systems to schedule those tasks among all resources of the platform. The real challenge is to design efficient schedulers for such runtimes to make effective utilization of all resources. Developing good schedulers, even for a single hybrid node, and analyzing them can thus have a strong impact on the performance of current HPC systems. We consider the problem of scheduling dense linear algebra applications on fully hybrid platforms made of CPUs and GPUs. The relative performance of CPU and GPU highly depends on the sub-routine. For instance, GPUs are much more efficient to process matrix-matrix multiplications than matrix factorizations. In this thesis, we analyze the performance of static and dynamic scheduling strategies and we propose a set of intermediate strategies, by adding static (resp. dynamic) features into dynamic (resp. static) strategies. A resource centric dynamic scheduler, HeteroPrio, which is based on affinity between tasks and resources, has been proposed recently for a set of small independent tasks on two types of resources. We extend and analyze this scheduler for general task graphs first on two types of resources and then on more than two types of resources. Additionally, we provide approximation ratios and worst case examples of HeteroPrio for a set of independent tasks on different platform sizes.
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Submitted on : Tuesday, June 13, 2017 - 4:26:06 PM
Last modification on : Saturday, July 25, 2020 - 3:08:28 AM
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  • HAL Id : tel-01538516, version 1



Suraj Kumar. Scheduling of Dense Linear Algebra Kernels on Heterogeneous Resources. Other [cs.OH]. Université de Bordeaux, 2017. English. ⟨NNT : 2017BORD0572⟩. ⟨tel-01538516⟩



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