DeepSim: Cluster Level Behavioral Simulation Model for Deep Learning.

We are witnessing an explosion of AI based use cases driving the computer industry, and especially datacenter and server architectures. As Intel faces fierce competition in this emerging technology space, it is important that architecture definitions and directions are driven with data from proper tools and methodologies, and insights are drawn from end-to-end holistic analysis at the datacenter levels. In this paper, we introduce DeepSim, a cluster-level behavioral simulation model for deep learning. DeepSim, which is based on the Intel CoFluent simulation framework, uses timed behavioral models to simulate complex interworking between compute nodes, networking, and storage at the datacenter level, providing a realistic performance model of a real-world image recognition applications based on the popular Deep Learning Framework Caffe. The end-to-end simulation data from DeepSim provides insight which can be used for architecture analysis driving future datacenter architecture directions. DeepSim enables scalable system design, deployment, and capacity planning through accurate performance insights. Results from preliminary scaling studies (e.g. node scaling and network scaling) and what-if analyses (e.g., Xeon with HBM and Xeon Phi with dual OPA) are presented in this paper. The simulation results are correlated well with empirical measurements, achieving an accuracy of 95%.