Distributed Machine Learning on vSphere leveraging NVIDIA vGPU and Mellanox PVRDMA - Virtualize Applications

While virtualization technologies have proven themselves in the enterprise with cost effective, scalable and reliable IT computing, High Performance Computing (HPC) however has not evolved and is still bound to dedicating physical resources to obtain explicit runtimes and maximum performance. VMWare has developed technologies to effectively share accelerators for compute and networking. It is also possible to provision multiple GPUs to a single VM, enabling maximum GPU acceleration and utilization. With the impending end to Moore's law, the spark that is fueling the current revolution in deep learning is having enough compute horsepower to train neural-network based models in a reasonable amount of time The needed compute horsepower is derived largely from GPUs, which NVIDIA began optimizing for deep learning since 2012. The latest GPU architecture from NVIDIA is Turing, available with T4 as well as the RTX 6000 and RTX 8000 GPUs, which all support virtualization. .