Over the past years there has been significant enthusiasm for development of parallel computing on Graphics Processing Units (GPU) which have now become powerful and affordable hardware equipping data centers and research clusters. Our earlier research has explored the ways to exploit the parallel compute performance of the GPU along the CPU in the same cluster. We have proposed a model for processing distributed machine learning tasks leveraging both the CPU and the GPU equipped on the nodes. Still in this direction, we present in this paper our approach for optimizing the performance of the previously proposed framework. We then further present our approach for integrating this processing model into a more general dataflow graph processing framework by extending it with support for GPU tasks and resources. In addition we have developed a k-nearest neighbors implementation demonstrating all the features. We then present our model based on flow networks for the efficient scheduling on this heterogeneous framework.

Nam-Luc Tran, Sabri Skhiri, Arnaud Schils, and Egar Isaac Hiroshi Leon Saiki, An Approach for Maximizing Performance on Heterogeneous Clusters of CPU and GPU. EURA NOVA technical series.

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