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Performance Portability of a GPU Enabled Factorization with the DAGuE Framework

George Bosilca, Aurelien Bouteiller, Thomas Herault, Pierre Lemarinier, Narapat Ohm Saengpatsa, Stanimire Tomov, Jack J. Dongarra
Innovative Computing Laboratory, the University of Tennessee
IEEE International Conference on Cluster Computing (CLUSTER), 2011

@inproceedings{bosilca2011performance,

   title={Performance Portability of a GPU Enabled Factorization with the DAGuE Framework},

   author={Bosilca, G. and Bouteiller, A. and Herault, T. and Lemarinier, P. and Saengpatsa, N.O. and Tomov, S. and Dongarra, J.J.},

   booktitle={Cluster Computing (CLUSTER), 2011 IEEE International Conference on},

   pages={395–402},

   year={2011},

   organization={IEEE}

}

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Performance portability is a major challenge faced today by developers on heterogeneous high performance computers, consisting of an interconnect, memory with nonuniform access, many-cores and accelerators like GPUs. Recent studies have successfully demonstrated that dense linear algebra operations can be efficiently handled by runtime systems using a DAG representation. In this work, we present the GPU subsystem of the DAGuE runtime, and assess, on the Cholesky factorization test case, the minimal efforts required by a programmer to enable GPU acceleration in the DAGuE framework. The performance achieved by this unchanged code, on a variety of heterogeneous and distributed many cores and GPU resources, demonstrates the desired performance portability.
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