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Ready Solutions Engineering Test Results
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Figure 3. Scaling Xeon 7230 KNL using Dell NFS Storage Solution
The scalability results on the KNL cluster are shown in Figure 3. The results are similar to SKL results in Figure 2. For this test, batch
size was able to remain constant due to the smaller number of nodes and the fact that a smaller batch size was optimal on KNL
systems. With multi-node runs some performance is lost due to threads being needed for communication, and not pure computation as
with single node tests.
Conclusions and Future Work
For this blog we have focused on single node deep learning training performance comparing a range of different Intel CPU models and
generations, and conducted initial scaling studies for both SKL and KNL clusters. Our key takeaways are summarized as follows:
Intel Caffe with Intel MLSL scales to hundreds of nodes.
Skylake Gold 6148, 6150, and 6152 processors offer similar performance to Platinum SKUs.
KNL performance is also similar to Platinum SKUs.
Our future work will focus on other aspects of deep learning solutions including performance of other frameworks, inference
performance, and I/O considerations. TensorFlow is a very popular framework which we did not discuss here but will do so in a future
part of this blog series. Inferencing is a very important part of the workflow, as a model must be deployed for it to be of use! Finally we’ll
also compare the various storage options and tradeoffs as well as discuss the I/O patterns (network and storage) of TensorFlow and
Intel Caffe.