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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.

Training Time Reduction from Hours to Minutes

Training time is the performance metric used in supervised machine learning—it is the amount of time a computer takes to learn how to solve the given problem. In supervised machine learning, the computer is given data in which the answer can be found. So, supervised learning infers a model from the available, or labelled training data.

Our first machine learning benchmark is a simple demo model in the TensorFlow library. The model classifies handwritten digits from the MNIST dataset. Each digit is a handwritten number that is centered within a consistently sized grayscale bitmap. The MNIST database of handwritten digits contains 60,000 training examples and has a test set of 10,000 examples.

First, we compare training times for the model using two different virtual machine configurations:

  • NVIDIA GRID Configuration (vg1c12m60GB): 1 vGPU, 12 vCPUs, 60GB memory, 96GB of SSD storage, CentOS 7.2
  • No GPU configuration (g0c12m60GB): No GPU, 12 vCPUs, 60GB memory, 96GB of SSD storage, CentOS 7.2
MNIST vg1c12m60GB
1 vGPU 
(secs)
g0c12m60GB
No GPU (secs)
Normalized Training Time
(wrt vg1c12)
1.0 10.06
CPU Utilization 8% 43%

The above table shows that vGPU reduces the training time by 10 times. The CPU utilization also goes down 5 times. See the graphs below.

01-training-time-mnist

02-mnist-cpu-util

Scaling from One GPU to Four GPUs

This machine learning benchmark is made up of two components:

We use the metric of images per second (images/sec) to compare the different configurations as we scale from a single GPU to 4 GPUs. The metric of images/second denotes the number of images processed per second in training the model.

Our host has two NVIDIA M60 cards. Each card has 2 GPUs. We present the performance results for scaling up from 1 GPU to 4 GPUs.

You can configure the GPUs in two modes:

  • DirectPath I/O passthrough: In this mode, the host can be configured to have 1 to 4 GPUs in a DirectPath I/O passthrough mode. A virtual machine running on the host will have access to 1 to 4 GPUs in passthrough mode.
  • GRID vGPU mode: For machine learning workloads, each VM should be configured with the highest profile vGPU. Since we have M60 GPUs, we configured VMs with vGPU type M60-8q. M60-8q implies one VM/GPU.

DirectPath I/O

First we focus on DirectPath I/O passthrough mode as we scale from 1 GPU to 4 GPUs.

CIFAR-10 g1c48m60GB g2c48m60GB g4c48m60GB
   1 GPU  2 GPUs  4 GPUs
Normalized Images/sec in Thousands (w.r.t. 1 GPU) 1.0 2.04 3.74
CPU Utilization 25% 44% 71%

As the above table shows, the images processed per second improves almost linearly with the number of GPUs on the host. This means that the number of images processed becomes greater with each increase in the number of GPUs in an amount that is expected. 1 GPU sets the normalized data at 1,000 image/sec. We expect 2 GPUs to handle about double that of 1 GPU, which the graph shows. Next, we see that 4 GPUs can handle nearly 4,000 images/sec.

03-cifar10-images-per-sec

Host CPU utilization also increases linearly, as shown in the following graph.

04-cifar10-cpu-used

Single GPU DirectPath I/O vs GRID vGPU mode

Now, we present comparison of performance results for DirectPath IO and GRID vGPU mode.

Since each VM can have only one vGPU in GRID vGPU mode, we first present the results for 1 GPU configuration in DirectPath IO mode with vGPU mode.

 

MNIST g1c48m60GB vg1c48m60GB
(Lower Is Better) DirectPath I/O GRID vGPU
Normalized Training Times 1.0 1.05

 

CIFAR-10 g1c48m60GB vg1c48m60GB
(Higher  Is Better) DirectPath I/O GRID vGPU
Normalized Images/sec 1.0 0.87

 

The above tables show that one GPU configuration in DirectPath I/O and GRID mode vGPU are very close in performance. We suggest you use GRID vGPU mode because it offers the benefits of virtualization.

Multi-GPU DirectPath I/O vs Multi-VM DirectPath I/O vs Multi-VMs in GRID vGPU mode

Now we move on to multi-GPU performance results for DirectPath I/O and GRID vGPU mode. In DirectPath I/O mode, a VM can be configured with all the GPUs on the host.  In our case, we configured the VM with 4 GPUs. In GRID vGPU mode, each VM can have at most 1 GPU. Therefore, we compare the results of 4 VMs running the same job with a VM using 4 GPUs using Direct Path I/O.

CIFAR-10 g4c48m60GB g1c12m16GB (4-vms) vg1c12m16GB(4-vms)
DirectPath I/O DirectPath I/O (4 VMs) GRID vGPU ( 4 VMs)
Normalized Images/Sec
(Higher Is Better)
1.0 0.98 0.92
CPU Utilization 71% 68% 69%

05-cifar10

06-cifar10

The multi-GPU DirectPath I/O mode configuration performs better. If your workload requirement is low latency or requires a short training time, you should use multi-GPU DirectPath I/O mode. However, other virtual machines will not be able use the GPUs on the host at the same time. If you can tolerate longer latencies or training times, we recommend using a 1-GPU configuration.  GRID vGPU mode enables the benefits of virtualization: flexibility and elasticity.

Takeaways

  • GPUs bring the training times of machine learning algorithms from hours to minutes.
  • You can use NVIDIA GPUs in two modes in the VMware vSphere environment for machine learning applications:
    • DirectPath I/O passthrough mode
    • GRID vGPU mode
  • You should use GRID vGPU mode with the highest vGPU profile. The highest vGPU profile implies 1 VM/GPU, thus giving the virtual machine full access to the entire GPU.
  • For a 1-GPU configuration, the performance of the machine learning applications in GRID vGPU mode is comparable to DirectPath I/O.
  • For the shortest training time, you should use a multi-GPU configuration in DirectPath I/O mode.
  • For running multiple machine learning jobs simultaneously, you should use GRID vGPU mode. This configuration offers a higher consolidation of virtual machines and leverages the flexibility and elasticity benefits of VMware virtualization.

References

Configuration Details

Host Configuration

Model Dell PowerEdge R730
Processor Type Intel® Xeon® CPU E5-2680 v3 @ 2.50GHz
CPU Cores 24 CPUs, each @ 2.499GHz
Processor Sockets 2
Cores per Socket 12
Logical Processors 48
Hyperthreading Active
Memory 768GB
Storage Local SSD (1.5TB), Storage Arrays, Local Hard Disks
GPUs 2x M60 Tesla

Software Configuration

ESXi  6.0.0, 3500742
Guest OS CentOS Linux release 7.2.1511 (Core)
CUDA Driver 7.5
CUDA Runtime 7.5

VM Configurations

VM vCPUs Memory Storage GPUs Guest OS Mode
g0xc12m60GB 12 vCPUs 60GB 1x96GB (SSD) 0 CentOS 7.2 No GPU
g1xc12m60GB 12 vCPUs 60GB 1x96GB (SSD) 1 CentOS 7.2 DirectPath I/O
g2xc48m60GB 48 vCPUs 60GB 1x96GB

(SSD)

2 CentOS 7.2 DirectPath I/O
g4xc48m60GB 48 vCPUs 60GB 1x96GB

(SSD)

4 CentOS 7.2 DirectPath I/O
vg1xc12m60GB 12 vCPUs 60GB 1x96GB (SSD) 1 CentOS 7.2 GRID vGPU
g1c12m16GB 12 vCPUs 16GB 1x96GB

(SSD)

1 CentOS 7.2 DirectPath I/O
vg1c12m16GB 12 vCPUs 16GB 1x96GB

(SSD)

1 CentOS 7.2 GRID vGPU