Dell PowerEdge C4140 Deep Learning Performance Comparison - Scale-up vs. Scale - Page 27

Transform batch n+2 on CPU

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Deep Learning Performance: Scale-up vs Scale-out Figure 20: PowerEdge C4140-V100-SXM2- Configuration-K vs PowerEdge C4140-V100-SXM2 Configuration-M As shown in Figure 21 below, it shows that the number of CPU cores does play a role in terms of throughput. And the biggest difference is when running AlexNet. 7.1.8 What role does CPU play in Deep learning? The CPU plays a major role in the initial phase called data preprocessing. The steps below show an instruction pipeline, with the following 4 instructions happening in parallel: a. Train on batch n (on GPUs) b. Copy batch n+1 to GPU memory c. Transform batch n+2 (on CPU) d. Load batch n+3 from disk (on CPU) The loop for the data processing when training is: a. Load mini-batch b. Preprocess mini-batch c. Train on mini-batch Architectures & Technologies Dell EMC | Infrastructure Solutions Group 26

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Deep Learning Performance: Scale-up vs Scale-out
Architectures & Technologies
Dell
EMC
| Infrastructure Solutions Group
26
Figure 20: PowerEdge C4140-V100-SXM2- Configuration-K vs PowerEdge C4140-V100-SXM2
Configuration-M
As shown in
Figure 21
below, it shows that the number of CPU cores does play a role in terms of
throughput. And the biggest difference is when running AlexNet.
7.1.8
What role does CPU play in Deep learning?
The CPU plays a major role in the initial phase called data preprocessing. The steps below show
an instruction pipeline, with the following 4 instructions happening in parallel:
a.
Train on batch n (on GPUs)
b.
Copy batch n+1 to GPU memory
c.
Transform batch n+2 (on CPU)
d.
Load batch n+3 from disk (on CPU)
The loop for the data processing when training is:
a.
Load mini-batch
b.
Preprocess mini-batch
c.
Train on mini-batch