Deep Learning with COTS HPC Systems

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Provided by: Stanford Technology Ventures Program
Topic: Hardware
Format: PDF
Scaling up deep learning algorithms has been shown to lead to increased performance in benchmark tasks and to enable discovery of complex high-level features. Recent, efforts to train extremely large networks (with over 1 billion parameters) have relied on cloudlike computing infrastructure and thousands of CPU cores. In this paper, the authors present technical details and results from the authors' system based on Commodity Off-The-Shelf High Performance Computing (COTS HPC) technology: a cluster of GPU servers with Infiniband interconnects and MPI.
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