19165

Blink: Fast and Generic Collectives for Distributed ML

Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil Devanur, Ion Stoica
Microsoft Research
arXiv:1910.04940 [cs.DC], (11 Oct 2019)

@misc{wang2019blink,

   title={Blink: Fast and Generic Collectives for Distributed ML},

   author={Guanhua Wang and Shivaram Venkataraman and Amar Phanishayee and Jorgen Thelin and Nikhil Devanur and Ion Stoica},

   year={2019},

   eprint={1910.04940},

   archivePrefix={arXiv},

   primaryClass={cs.DC}

}

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Model parameter synchronization across GPUs introduces high overheads for data-parallel training at scale. Existing parameter synchronization protocols cannot effectively leverage available network resources in the face of ever increasing hardware heterogeneity. To address this, we propose Blink, a collective communication library that dynamically generates optimal communication primitives by packing spanning trees. We propose techniques to minimize the number of trees generated and extend Blink to leverage heterogeneous communication channels for faster data transfers. Evaluations show that compared to the state-of-the-art (NCCL), Blink can achieve up to 8x faster model synchronization, and reduce end-to-end training time for image classification tasks by up to 40%.
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