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Hierarchical DAG Scheduling for Hybrid Distributed Systems

Wei Wu, Aurelien Bouteiller, George Bosilca, Mathieu Faverge, Jack Dongarra
The University of Tennessee, Knoxville, USA
29th IEEE International Parallel & Distributed Processing Symposium, 2015

@inproceedings{wu:hal-01078359,

   title={Hierarchical DAG Scheduling for Hybrid Distributed Systems},

   author={Wu, Wei and Bouteiller, Aurelien and Bosilca, George and Faverge, Mathieu and Dongarra, Jack},

   url={https://hal.inria.fr/hal-01078359},

   booktitle={29th IEEE International Parallel & Distributed Processing Symposium},

   address={Hyderabad, India},

   year={2015},

   month={May},

   keywords={PaRSEC runtime ; GPU ; dense linear algebra; heterogeneous architecture},

   hal_id={hal-01078359},

   hal_version={v1}

}

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Accelerator-enhanced computing platforms have drawn a lot of attention due to their massive peak com-putational capacity. Despite significant advances in the pro-gramming interfaces to such hybrid architectures, traditional programming paradigms struggle mapping the resulting multi-dimensional heterogeneity and the expression of algorithm parallelism, resulting in sub-optimal effective performance. Task-based programming paradigms have the capability to alleviate some of the programming challenges on distributed hybrid many-core architectures. In this paper we take this concept a step further by showing that the potential of task-based programming paradigms can be greatly increased with minimal modification of the underlying runtime combined with the right algorithmic changes. We propose two novel recursive algorithmic variants for one-sided factorizations and describe the changes to the PaRSEC task-scheduling runtime to build a framework where the task granularity is dynamically adjusted to adapt the degree of available parallelism and kernel effi-ciency according to runtime conditions. Based on an extensive set of results we show that, with one-sided factorizations, i.e. Cholesky and QR, a carefully written algorithm, supported by an adaptive tasks-based runtime, is capable of reaching a degree of performance and scalability never achieved before in distributed hybrid environments.
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