Dell PowerEdge C4140 Deep Learning Performance Comparison - Scale-up vs. Scale - Page 38

Deep Learning Performance: Scale-up vs Scale-out Figure 31: Training with PowerEdge C4140-K-V100-16&32GB-SXM2 (8 GPUs) - multi-node versus Non-Dell EMC SN_8x-V100-16GB-SXM2 SN_8X V100_16GB- SXM2 MN- PowerEdge C4140- % Diff K-V100-SXM2 (16Gb &32GB)-IntelXeon4116 Inception-v4 1606 1625 -1.21% VGG-19 2449 2406 1.78% VGG-16 2762 2820 -2.03% Inception-v3 3077 2845 8.16% ResNet-50 4852 4500 7.81% GoogLeNet 7894 8754 -9.82% AlexNet 16977 12145 39.79% Table 5: 8x GPU Comparison between PowerEdge C4140-K multi-node and 8X SXM2 As seen from the table above, using PowerEdge C4140 with SXM2 shows pretty good performance across various pre-trained neural models. The most common ones i.e. ResNet-50 and Inception-v3 show performance within 8% of 8X SXM2. The only exception is AlexNet where it shows quite a bit of difference between 8X SXM2 and PowerEdge C4140. The good performance shown by PowerEdge C4140 in multi node mode, comparable to a single node server 8x V100-16GB, was reached after the right software stack configuration with the Architectures & Technologies Dell EMC | Infrastructure Solutions Group 37