Purine: A Bi-Graph Based Deep Learning Framework

Min Lin, Shuo Li, Xuan Luo, Shuicheng Yan

ICLR 2015

/iclr/2015/lin2015iclr-purine/

Abstract

In this paper, we introduce a novel deep learning framework, termed Purine. In Purine, a deep network is expressed as a bipartite graph (bi-graph), which is composed of interconnected operators and data tensors. With the bi-graph abstraction, networks are easily solvable with event-driven task dispatcher. We then demonstrate that different parallelism schemes over GPUs and/or CPUs on single or multiple PCs can be universally implemented by graph composition. This eases researchers from coding for various parallelization schemes, and the same dispatcher can be used for solving variant graphs. Scheduled by the task dispatcher, memory transfers are fully overlapped with other computations, which greatly reduce the communication overhead and help us achieve approximate linear acceleration.

ICLR Semantic Scholar

Cite

Text

Lin et al. "Purine: A Bi-Graph Based Deep Learning Framework." International Conference on Learning Representations, 2015.

Markdown

[Lin et al. "Purine: A Bi-Graph Based Deep Learning Framework." International Conference on Learning Representations, 2015.](https://mlanthology.org/iclr/2015/lin2015iclr-purine/)

BibTeX

@inproceedings{lin2015iclr-purine,
  title     = {{Purine: A Bi-Graph Based Deep Learning Framework}},
  author    = {Lin, Min and Li, Shuo and Luo, Xuan and Yan, Shuicheng},
  booktitle = {International Conference on Learning Representations},
  year      = {2015},
  url       = {https://mlanthology.org/iclr/2015/lin2015iclr-purine/}
}