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Asymptotic Peak Utilisation in Heterogeneous Parallel CPU/GPU Pipelines: A Decentralised Queue Monitoring Strategy

Michael T. Garba, Horacio Gonzalez-Velez
Robert Gordon University, UK
High-level programming for heterogeneous and hierarchical parallel systems (HLPGPU), 2012

@article{garba2012asymptotic,

   title={Asymptotic Peak Utilisation in Heterogeneous Parallel CPU/GPU Pipelines: A Decentralised Queue Monitoring Strategy},

   author={Garba, M.T.},

   year={2012}

}

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Heterogeneous parallel computing has become an unavoidable consequence of the emergence of GeneralPurpose computing on graphics processing units (GPGPU). The characteristics of a Graphics Processing Unit (GPU)-including significant memory transfer latency and complex performance characteristics-demand new approaches to ensuring that all available computational resources are geared towards optimal utilisation. This paper considers the simple case of a divisible workload based on widely-used numerical linear algebra routines and considers the challenges that present themselves when an attempt is made to efficiently use all resources available with a view in balancing the CPU and GPU utilisation. We suggest a possible queue monitoring strategy that facilitates resource usage for applications that fit the pipeline parallel architectural pattern on heterogeneous multicore/multi-node CPU and GPU systems.
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