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FusionCloud 6.3.0 Solution Description 05

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Huawei uses machine translation combined with human proofreading to translate this document to different languages in order to help you better understand the content of this document. Note: Even the most advanced machine translation cannot match the quality of professional translators. Huawei shall not bear any responsibility for translation accuracy and it is recommended that you refer to the English document (a link for which has been provided).
GPU-type ECSs

GPU-type ECSs

GPU ECSs provide outstanding floating-point computing capabilities. They are suitable for scenarios that require real-time, highly concurrent massive computing.

GPU-type ECSs are divided into graphics-accelerated ECSs (G series, whose virtualization type is Xen) and computing-accelerated ECSs (P series, whose virtualization type is KVM). Among GPU-type ECSs:

  • G series ECSs are suitable for 3D animation rendering and CAD.

    G series ECSs are divided into G1 ECSs, which use GPU virtualization, and G2 ECSs, which use GPU SR-IOV.

  • P series ECSs are designed for deep learning, scientific computing, and CAE.

Application Scenarios

  • Applications

    Deep learning, scientific computing, CAE, 3D animation rendering, and CAD

  • Scenario characteristics

    Highly real-time, highly concurrent, and massive computing

  • Application scenarios
    • P series ECSs are suitable for artificial intelligence: Each GPU contains thousands of computing units, providing outstanding parallel computing capabilities. P series ECSs have been optimized for deep learning, supporting massive computing within a short period of time.
    • P series ECSs are suitable for scientific computing: Scientific computing has strict requirements on double-precision computing. During computing emulation, a large number of computing resources are used, and large volumes of data are generated. Therefore, scientific computing also has strict requirements on storage bandwidth and latency. P series ECSs meet these requirements.
    • G series ECSs are suitable for graphic workstation: G series ECSs provide outstanding computing capabilities for professional CAD, video rendering, and graphics processing.
    NOTE:
    • If FCD is used to set up the environment during installation and GPU ECSs were not planned previously but you need to use them after the environment is set up, log in to the physical servers to enable intel_iommu. For details, see Product Management > FusionSphere OpenStack > Configuring GPU and USB Parameters of KVM-Based Compute Nodes in FusionCloud 6.3.0 O&M Guide.
    • To use GPU ECSs, log in to DMK and check whether the value of is_supported_gpu_ecs is true.

Graphics-accelerated ECSs

Graphics-accelerated ECSs (G series ECSs, whose virtualization type is Xen) are divided into G1 ECSs, which use GPU virtualization, and G2 ECSs, which use GPU SR-IOV.

  • G1 ECSs are based on NVIDIA GRID virtual GPUs and provide economical graphics acceleration. G1 ECSs use the NVIDIA Tesla M60 GPU and support DirectX and OpenGL. The ECSs have a maximum of 8 GB video RAM and 4,096 x 2,160 resolution, and are suitable for applications that require high performance in graphics rendering.
  • G2 ECSs are based on NVIDIA Tesla M60 hardware passthrough and provide graphics acceleration and single-precision computing with a maximum of 8 GB GPU memory and 4,096 x 2,160 resolution. They support DirectX, OpenGL, CUDA, and OpenCL, provide 2,048 CUDA cores, and are suitable for media editing, 3D rendering, and transcoding.
Scope of Support
  • G1 ECSs support:
    • NVIDIA M60 GPUs
    • Graphics acceleration applications
    • GPU hardware virtualization (vGPUs)
    • Application flow identical to common ECSs
    • A maximum of 8 GB GPU memory and 4,096 x 2,160 resolution for processing videos
  • G2 ECSs support:
    • NVIDIA M60 GPUs
    • Graphics acceleration applications
    • GPU hardware passthrough
    • Enhanced SR-IOV network performance and high bandwidths
    • A maximum of 8 GB GPU memory and 4,096 x 2,160 resolution for processing videos
    • DirectX, OpenGL, CUDA, and OpenCL
    • Up to 2048 CUDA cores
Notes
  • G1 ECSs
    • G1 ECSs do not support flavor change.
    • G1 ECSs support the following OSs:
      • Windows Server 2008 R2 Enterprise SP1 64bit
      • Windows Server 2012 R2 Standard 64bit
      • Windows Server 2016 DataCenter
      • Windows Server 2008
      • Windows Server 2012
      • Windows Server 2016
    • If a G1 ECS is created using a private image, install a GPU driver on the ECS after the ECS creation. To download the driver, log in at http://www.nvidia.com/grid-eval, set the NVIDIA GRID version to 4.1, and select the GRID for UVP software package. The operations are as follows:
      1. Check whether NVIDIA is used for the first time:
        1. If yes, go to 2.
        2. If no, go to 4.
      2. Obtain the Product Activation Key (PAK) from the email indicating successful registration with NVIDIA, as shown in Figure 16-1.
        Figure 16-1 PAK
      1. Enter the PAK obtained in 1.b on the Redeem Product Activation Keys page and click Redeem.
        Figure 16-2 Redeem Product Activation Keys
      1. Click the Archived Versions tab.
        Figure 16-3 Logging in to the official NVIDIA website
      2. Log in at the official NVIDIA website as prompted and choose Software & Services > Product Information.

      3. Click the Archived Versions tab.
      4. Click NVIDIA GRID of version 4.1.
      5. On the Product Download page, click GRID for UVP.
    • If you log in to a G1 ECS using MSTSC, graphics acceleration will fail. This is because MSTSC replaces the WDDM GPU driver with a non-accelerated remote desktop display driver. In such an event, you must use other methods to log in to the ECS, such as VNC.

      If the remote login function available on the management console fails to meet your service requirements, you must install a suitable remote login tool on the ECS.

  • G2 ECSs
    • G2 ECSs do not support flavor change.
    • G2 ECSs support the following OSs:
      • Windows Server 2008 R2 Enterprise SP1 64bit
      • Windows Server 2012 R2 Standard 64bit
      • Windows Server 2008
      • Windows Server 2012
    • If a G2 ECS is created using a private image, install a GPU driver during the private image creation. Otherwise, install the GPU driver after creating the ECS.

      To download the GPU driver, log in at http://www.nvidia.com/Download/index.aspx?lang=en-us. You are advised to select the latest CUDA toolkit version.

    After the GPU driver is installed, run the following command to switch the GPU working mode and restart the ECS (for example, the GPU driver is installed in C:\Program Files\NVIDIA Corporation\NVSMI\nvidia-smi.exe):

    "C:\Program Files\NVIDIA Corporation\NVSMI\nvidia-smi.exe" -dm 0

    • If a G2 ECS is created using a private image, install an SR-IOV driver during the private image creation. Otherwise, install it after the ECS is created.

      To download the SR-IOV driver, log in at https://downloadcenter.intel.com/search?keyword=Intel++Ethernet+Connections+CD. You are advised to select version 20.4.1 or later.

    • If you log in to a G2 ECS using MSTSC, graphics acceleration will fail. This is because MSTSC replaces the WDDM GPU driver with a non-accelerated remote desktop display driver. In such an event, you must use other methods to log in to the ECS, such as VNC.
    • G2 ECSs do not support remote login. If you need to log in to a G2 ECS using VNC, install a VNC server on the ECS before login.
Flavor
Table 16-4 Xen-based GPU ECSs

Type

vCPU

Memory (GB)

Flavor Name

Basic graphics processing G1

4

8

g1.xlarge

8

16

g1.2xlarge

16

32

g1.4xlarge

Accelerated graphics processing G2

8

64

g2.2xlarge

Computing-accelerated ECSs

PI1 ECSs use NVIDIA Tesla P4 GPUs dedicated for AI inference, providing outstanding real-time inference. Working with INT8 calculators of NVIDIA Tesla P4 GPUs, PI1 ECSs have shortened the inference latency by 15 times.

PI2 ECSs use NVIDIA Tesla P40 GPUs dedicated for ultra-high AI inference throughput, providing outstanding real-time inference. The single-precision floating-point computing performance and INT8 integer computing performance of NVIDIA Tesla P40 GPUs are twice times faster than those of NVIDIA Tesla P4 GPUs. When processing deep learning workloads, a server configured with eight NVIDIA Tesla P40 GPUs equals over 100 servers configured only with CPUs.

Configured with hardware decoding engines, PI1 and PI2 ECSs concurrently support real-time 35-channel HD video transcoding and inference.

PI1 ECSs

Features

PI1 ECSs have the following features:
  • NVIDIA Tesla P4 GPUs
  • Up to 5.5 TFLOPS by a single GPU for single precision
  • Up to 22 TOPS INT8 by a single GPU
  • 8 GB ECC GPU memory with a bandwidth of 192 Gbit/s by a single GPU
  • Hardware video encoding and decoding engines embedded in GPUs for concurrent real-time 35-channel HD video transcoding and inference
Notes
  • Ensure that the NVIDIA driver has been installed on the image used to create a PI1 ECS. If not, install the driver after the PI1 ECS is created for computing acceleration. To download the driver, log in at http://www.nvidia.com/Download/Find.aspx?lang=en. Figure 16-4 shows parameter settings.
    Figure 16-4 NVIDIA Driver Downloads
  • PI1 ECSs do not support flavor change.
  • PI1 ECSs support the following OSs:
    • Ubuntu Server 14.04 64bit
    • CentOS 7.3 64bit
Flavors
Table 16-5 Flavors of PI1 computing-accelerated ECSs

Type

vCPU

Memory (GB)

Flavor Name

GPU

PI1 computing-accelerated ECSs

8

64

PI1.2xlarge.8

1×P4

16

128

PI1.4xlarge.8

2×P4

32

256

PI1.8xlarge.8

4×P4

PI2 ECSs
Notes
  • Ensure that the NVIDIA driver has been installed on the image used to create a PI2 ECS. If not, install the driver after the PI2 ECS is created for computing acceleration.
  • PI2 ECSs support the following OSs:
    • Ubuntu Server 16.04 64bit
    • Debian 9.0 64bit
    • CentOS 7.4 64bit
    • EulerOS 2.2
    • Window Server 2012 R2 64-bit (Enterprise Edition)
Flavors
Table 16-6 Flavors of PI2 computing-accelerated ECSs

Type

vCPU

Memory (GB)

Flavor Name

GPU

PI2 computing-accelerated

8

64

PI2.2xlarge.8

1×P40

16

128

PI2.4xlarge.8

2×P40

32

256

PI2.8xlarge.8

4×P40

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Updated: 2019-04-23

Document ID: EDOC1100026685

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