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