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Monthly Archives: October 2016

Understanding vSphere DRS Performance – A White Paper

VMware vSphere Distributed Resource Scheduler (DRS) is responsible for placement of Virtual Machines and balancing of resources in a cluster. The key driver for DRS is VM/Application happiness, and it achieves this by effective VM placement and efficient load balancing. We have a new white paper, which tries to explain how DRS works in basic scenarios and how it can be tuned to behave differently for specific scenarios.

The white paper talks about the factors that influence DRS decisions and provides some useful insights into different parameters that can be tuned in specific scenarios to make DRS more effective. It also explains how to monitor DRS to better understand its behavior.

It covers DRS behavior in specific scenarios with some case studies. Some of these studies are around

  •  VM Consumed vs. Active Memory – How it impacts DRS behavior.
  •  Impact of VM overrides on cluster balance.
  •  Prerequisite moves during initial placement.
  •  Using shares to prioritize cluster resources.

The paper provides knowledge about the factors that affect DRS behavior and helps understand how DRS does what it does. This knowledge, along with monitoring and troubleshooting tips, including real case studies, will help tune DRS clusters for optimum performance.

Machine Learning on VMware vSphere 6 with NVIDIA GPUs

by Uday Kurkure, Lan Vu, and Hari Sivaraman

Machine learning is an exciting area of technology that allows computers to behave without being explicitly programmed, that is, in the way a person might learn. This tech is increasingly applied in many areas like health science, finance, and intelligent systems, among others.

In recent years, the emergence of deep learning and the enhancement of accelerators like GPUs has brought the tremendous adoption of machine learning applications in a broader and deeper aspect of our lives. Some application areas include facial recognition in images, medical diagnosis in MRIs, robotics, automobile safety, and text and speech recognition.

Machine learning workloads have also become a critical part in cloud computing. For cloud environments based on vSphere, you can even deploy a machine learning workload yourself using GPUs via the VMware DirectPath I/O or vGPU technology.

GPUs reduce the time it takes for a machine learning or deep learning algorithm to learn (known as the training time) from hours to minutes. In a series of blogs, we will present the performance results of running machine learning benchmarks on VMware vSphere using NVIDIA GPUs.

Episode 1: Performance Results of Machine Learning with DirectPath I/O and NVIDIA GPUs

In this episode, we present the performance results of running machine learning benchmarks on VMware vSphere with NVIDIA GPUs in DirectPath I/O mode and on GRID virtual GPU (vGPU) mode.

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