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Dell High Performance Computing Solution Resources Owner's manual

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  • What is the Dell EMC PowerEdge R740?
    What are the benefits of using the Dell EMC PowerEdge R740 for deep learning training?
    What deep learning frameworks are supported on the Dell EMC PowerEdge R740?
    What kind of performance can I expect from the Dell EMC PowerEdge R740 for deep learning training?
    How scalable is the Dell EMC PowerEdge R740 for deep learning training?
Ready Solutions Engineering Test Results
Deep Learning Performance on R740 with V100-PCIe GPUs
Authors: Rengan Xu, Frank Han, Nishanth Dandapanthula
Dell EMC HPC Innovation Lab. February 2018
Overview
The Dell EMC PowerEdge R740 is a 2-socket, 2U rack server. The system features the Intel Skylake processors, up to 24 DIMMs, and
up to 3 double width or 6 single width GPUs. In our previous blog Deep Learning Inference on P40 vs P4 with SkyLake, we presented
the deep learning inference performance on Dell EMC’s PowerEdge R740 server with P40 and P4 GPUs. This blog will present the
performance of the deep learning training performance on single R740 with multiple V100-PCIe GPUs. The deep learning frameworks
we benchmarked include Caffe2, MXNet and Horovod+TensorFlow. Horovod is a distributed framework for TensorFlow. We used
Horovod because it has better scalability implementation (using MPI model) than TensorFlow, which has been explained in the article
“Meet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlow”. Table 1 shows the hardware configuration
and software details we tested. To test the deep learning performance and scalability on R740 server, we used the same neural network,
the same dataset and the same measurement as in our other deep learning blog series such as Scaling Deep Learning on Multiple V100
Nodes and Deep Learning on V100.
Table 1: The hardware configuration and software details
Platform
PowerEdge R740
CPU
2 x Intel Xeon 6150 @2.7GHz (Skylake)
Memory
192GB DDR4 @ 2667MHz
Shared Storage
9TB NFS through IPoIB on EDR Infiniband
GPU
V100-PCIe
Software and Firmware
Operating System
RHEL 7.3 x86_64
Linux Kernel
3.10.0-514.26.2.el7.x86_64
BIOS
2.4.2
CUDA compiler and GPU driver
CUDA 9.0 (387.26)
NCCL
2.0
Python
2.7.5
Deep Learning Libraries and Frameworks
CUDNN
7.0
Caffe2
0.8.1
MXNet
0.11.1
TensorFlow
1.4.0
Horovod
1.11.1
Performance Evaluation
Ready Solutions Engineering Test Results
The Figure 1, Figure 2 and Figure 3 show the Resnet50 performance and speedup of multiple V100 GPUs with Caffe2, MXNet and
TensorFlow, respectively. We can obtain the following conclusions based on these results:
Overall the performance of Resnet50 scales well on multiple V100 GPUs within one node. With 3 V100:
o Caffe2 achieved the speedup of 2.61x and 2.65x in FP32 and FP16 mode, respectively.
o MXNet achieved the speedup of 2.87x and 2.82x in FP32 and FP16 mode, respectively.
o Horovod+TensorFlow achieved the speedup of 2.12x in FP32 mode. (FP16 still under development)
The performance in FP16 mode is around 80%-90% faster than FP32 for both Caffe2 and MXNet. TensorFlow still has not
supported FP16 yet, so we will test its FP16 performance once this feature is supported.
Figure 1: Caffe2: Performance and speedup of V100
Figure 2: MXNet: Performance and speedup of V100
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Speedup
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MXNet Resnet50
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Caffe2 Resnet50
perf-FP32 perf-FP16 speedup-FP32 speedup-FP16
Copyright © 2017 Dell Inc. or its subsidiaries. All Rights Reserved. Dell, EMC, and other trademarks are trademarks of Dell Inc. or its subsidiaries. Other trademarks may be
the property of their respective owners. Published in the USA. Dell EMC believes the information in this document is accurate as of its publication date. The information is
subject to change without notice.
Ready Solutions Engineering Test Results
Figure 3: TensorFlow: Performance and speedup of V100
Conclusions
In this blog, we presented the deep learning performance and scalability of popular deep learning frameworks like Caffe2, MXNet and
Horovod+TensorFlow. Overall the three frameworks scale as expected on all GPUs within single R740 server.
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