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The benchmark was implemented using TensorFlow, which was also used for the implementation of the other machine learning benchmarks in our experiments. The VM in the last two cases has 12 virtual CPUs (vCPUs), 60GB RAM, and 96GB SSD storage. A GRID vGPU (that is, an M60-8Q vGPU profile with 8GB memory) inside a VM on vSphere 6.A DirectPath I/O GPU inside a VM on vSphere 6.A physical GPU installed on bare metal (this is the “native” configuration).To understand the performance impact of machine learning with GPUs using virtualization, we used a complex language modeling application-predicting next words given a history of previous words using a recurrent neural network (RNN) with 1500 Long Short Term Memory (LSTM) units per layer, on the Penn Treebank (PTB) dataset, which has: For example, a prior study shows that vector machine applications running on a virtualized cluster of 10 servers have a better execution time than running on bare metal. However, with the constant evolution of virtualization technologies, the performance gaps between bare metal and virtualization have almost disappeared, and, in some use cases, virtualized applications can achieve better performance than running on bare metal because of the intelligent and highly optimized resource utilization of hypervisors. Performance is one of the biggest concerns that keeps high performance computing (HPC) users from choosing virtualization as the solution for deploying HPC applications despite virtualization benefits such as reduced administration costs, resource utilization efficiency, energy saving, and security.

Episode 2: Machine Learning on vSphere 6 with NVIDIA GPUs.Episode 1: Performance Results of Machine Learning with DirectPath I/O and GRID vGPU.NVIDIA GRID vGPU that have been partially addressed in previous episodes: In addition, we extend the performance results of machine learning workloads using VMware DirectPath I/O (passthrough) vs. In our third episode of machine learning performance with vSphere 6.x, we look at the virtual GPU vs.