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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

Summary of content (3 pages)

PAGE 1
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.
PAGE 2
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: 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.
PAGE 3
Ready Solutions Engineering Test Results Horovod+TensorFlow Resnet50 800 739 2.12 3 700 2 513 1.47 500 400 2 348 1 1 300 200 Speedup Images/sec 600 1 100 0 0 1 V100 2 V100 3 V100 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.