Dell PowerEdge C4140 Deep Learning Performance Comparison - Scale-up vs. Scale - Page 5

Acknowledgements

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Deep Learning Performance: Scale-up vs Scale-out Acknowledgements We would like to acknowledge the following individuals, Jaime Edwards (Director of PowerEdge Advanced Engineering) for setting the direction of this project, April Berman (PowerEdge Acceleration Product Manager), Shreya Shah (PowerEdge C4140 Product Manager) and Trevor Montgomery (Enterprise Systems Group Business Development) for providing us the resources for this paper. We would also like to acknowledge:  Google TensorFlow team for helping us in debugging issues related to TensorFlow.  Uber Horovod team for explaining how to use their library in distributed training environment.  Nvidia CUDA support team & Nvidia account team for their expedited support.  Baidu Deep Bench support team  Nimbix engineering and support team. Architectures & Technologies Dell EMC | Infrastructure Solutions Group 4

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Deep Learning Performance: Scale-up vs Scale-out
Architectures & Technologies
Dell
EMC
| Infrastructure Solutions Group
4
Acknowledgements
We would like to acknowledge the following individuals, Jaime Edwards (Director of PowerEdge
Advanced Engineering) for setting the direction of this project, April Berman (PowerEdge
Acceleration Product Manager), Shreya Shah (PowerEdge C4140 Product Manager) and Trevor
Montgomery (Enterprise Systems Group Business Development) for providing us the resources
for this paper.
We would also like to acknowledge:
Google TensorFlow team for helping us in debugging issues related to TensorFlow.
Uber Horovod team for explaining how to use their library in distributed training
environment.
Nvidia CUDA support team & Nvidia account team for their expedited support.
Baidu Deep Bench support team
Nimbix engineering and support team.