Performance Implications of Big Data in Scalable Deep Learning: On the Importance of Bandwidth and Caching

@article{Hodak2018PerformanceIO,
  title={Performance Implications of Big Data in Scalable Deep Learning: On the Importance of Bandwidth and Caching},
  author={Miro Hodak and David Ellison and Peter Seidel and A. Dholakia},
  journal={2018 IEEE International Conference on Big Data (Big Data)},
  year={2018},
  pages={1945-1950}
}
Deep learning techniques have revolutionized many areas including computer vision and speech recognition. While such networks require tremendous amounts of data, the requirement for and connection to Big Data storage systems is often undervalued and not well understood. In this paper, we explore the relationship between Big Data storage, networking, and Deep Learning workloads to understand key factors for designing Big Data/Deep Learning integrated solutions. We find that storage and… Expand
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