Dell PowerEdge C4140 Deep Learning Performance Comparison - Scale-up vs. Scale - Page 40

Deep Learning Performance: Scale-up vs Scale-out 7.2.5 PowerEdge C4140-M Multi Node Training vs Non-Dell EMC 8x V100-16GB-SXM2 Figure 33. Training with PowerEdge C4140-M-V100-16GB-SXM2 (8 GPUs) - multi-node versus Non-Dell EMC SN_8x-V100-16GB-SXM2 In the Figure 33 above we can appreciate the throughput improvement when using a sever with a higher capacity CPU; as seen in the table below, almost all the models trained with C4140-MV100-16GB-SXM2 - CPU IntelXeon6148 (8 GPUs) - multi-node performed better than SN-8xV100. The exception was AlexNet which still performed under SN_8xV100; however, it improved its throughput significantly compared when trained with the server with C4140-K-V100-16GB-SXM2 - IntelXeon4116. See the summary in the below table SN_8X V100_16GB- SXM2 MN- PowerEdge C4140- % Diff M-V100-SXM2 16GB Inception-v4 1606 1993 19% VGG-19 2449 3205 24% VGG-16 2762 3734 26% Inception-v3 3077 3685 16% ResNet-50 4852 5904 18% GoogLeNet 7894 10801 27% AlexNet 16977 14969 -13% Table 6: Table 5: 8x GPU Comparison between PowerEdge C4140-M multi-node and 8X SXM2 Architectures & Technologies Dell EMC | Infrastructure Solutions Group 39