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Deep Learning Performance: Scale-up vs Scale-out
Architectures & Technologies Dell EMC | Infrastructure Solutions Group
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travels to other neurons which, in turn, increase or decrease their potentials in accordance with
this signal.
2.1 Deep Learning
Deep Learning consists of two phases: Training and inference. As illustrated in Figure 2, training
involves learning a neural network model from a given training dataset over a certain number of
training iterations and loss function. The output of this phase, the learned model, is then used in
the inference phase to speculate on new data [1].
The major difference between training and inference is training employs forward propagation
and backward propagation (two classes of the deep learning process) whereas inference mostly
consists of forward propagation. To generate models with good accuracy, the training phase
involves several training iterations and substantial training data samples, thus requiring many-
core CPUs or GPUs to accelerate performance.
Figure 2. Deep Learning phases
3 Background
With the recent advances in the field of Machine Learning and especially Deep Learning, it’s
becoming more and more important to figure out the right set of tools that will meet some of
the performance characteristics for these workloads.
Since Deep Learning is compute intensive, the use of accelerators like GPU become the norm.
But GPUs are costly and often it comes down to what is the performance difference between a
system with & without GPU.