From a533b978d3eaec4dbc49a6bb5649993831ac1913 Mon Sep 17 00:00:00 2001 From: guoyanyan Date: Tue, 25 Jul 2023 22:11:45 +0000 Subject: [PATCH] ecs_umn_0616_g7 Reviewed-by: Hasko, Vladimir Co-authored-by: guoyanyan Co-committed-by: guoyanyan --- docs/ecs/umn/en-us_topic_0038024694.html | 1 + docs/ecs/umn/en-us_topic_0041169567.html | 4 + docs/ecs/umn/en-us_topic_0097289624.html | 129 +++++++++++++++++++---- docs/ecs/umn/en-us_topic_0177512565.html | 86 +++++++++++++-- 4 files changed, 192 insertions(+), 28 deletions(-) diff --git a/docs/ecs/umn/en-us_topic_0038024694.html b/docs/ecs/umn/en-us_topic_0038024694.html index 2ef2aaaf1..c261e0303 100644 --- a/docs/ecs/umn/en-us_topic_0038024694.html +++ b/docs/ecs/umn/en-us_topic_0038024694.html @@ -6,6 +6,7 @@
  • Application scenarios

    OLAP and OLTP applications with hyper-threading enabled

    • +

    Specifications

    +
    Table 1 E3 ECS specifications

    Flavor

    vCPUs

    diff --git a/docs/ecs/umn/en-us_topic_0041169567.html b/docs/ecs/umn/en-us_topic_0041169567.html index f0bde6feb..6f254ac11 100644 --- a/docs/ecs/umn/en-us_topic_0041169567.html +++ b/docs/ecs/umn/en-us_topic_0041169567.html @@ -11,6 +11,10 @@

    2023-06-27

    Added Key Operations Supported by CTS.

    +

    2023-06-13

    +

    Modified the following content:

    +

    2023-05-10

    diff --git a/docs/ecs/umn/en-us_topic_0097289624.html b/docs/ecs/umn/en-us_topic_0097289624.html index 756632bf4..59142143d 100644 --- a/docs/ecs/umn/en-us_topic_0097289624.html +++ b/docs/ecs/umn/en-us_topic_0097289624.html @@ -6,18 +6,106 @@

    GPU-accelerated ECS Types

    Recommended: Computing-accelerated P2s, Inference-accelerated Pi2, and Graphics-accelerated Enhancement G6

    Available now: All GPU models except the recommended ones. If available ECSs are sold out, use the recommended ones.

    -
    +

    Graphics-accelerated Enhancement G7

    Overview

    +

    G7 ECSs use NVIDIA A40 GPUs and support DirectX, Shader Model, OpenGL, and Vulkan. Each GPU provides 48 GiB of GPU memory. Theoretically, G7 ECSs provide 37.4 TFLOPS of FP32 peak performance and 74.8 TFLOPS (sparsity disabled) or 149.6 TFLOPS (sparsity enabled) of TF32 peak tensor performance. They deliver two times the rendering performance and 1.4 times the graphics processing performance of RTX6000 GPUsto meet professional graphics processing requirements.

    +

    Select your desired GPU-accelerated ECS type and specifications.

    +

    Specifications

    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Table 1 G7 ECS specifications

    Flavor

    +

    vCPUs

    +

    Memory

    +

    (GiB)

    +

    Max./Assured Bandwidth

    +

    (Gbit/s)

    +

    Max. PPS

    +

    (10,000)

    +

    Max. NIC Queues

    +

    Max. NICs

    +

    GPUs

    +

    GPU Memory

    +

    (GiB)

    +

    Virtualization

    +

    g7.12xlarge.8

    +

    48

    +

    384

    +

    35/18

    +

    750

    +

    16

    +

    8

    +

    1 × NVIDIA-A40

    +

    1 × 48

    +

    KVM

    +

    g7.24xlarge.8

    +

    96

    +

    768

    +

    40/36

    +

    850

    +

    16

    +

    8

    +

    2 × NVIDIA-A40

    +

    2 × 48

    +

    KVM

    +
    +
    +

    G7 ECS Features

    +
    • CPU: 3rd Generation Intel® Xeon® Scalable 6348 processors (3.0 GHz of base frequency and 3.5 GHz of turbo frequency)
    • Graphics acceleration APIs
      • DirectX 12.07, Direct2D, DirectX Video Acceleration (DXVA)
      • Shader Model 5.17
      • OpenGL 4.68
      • Vulkan 1.18
      +
    • CUDA, DirectCompute, OpenACC, and OpenCL
    • A single card is equipped with 10,752 CUDA cores, 84 second-generation RT cores, and 336 third-generation Tensor cores.
    • Graphics applications accelerated
    • Heavy-load CPU inference
    • Application flow identical to common ECSs
    • Automatic scheduling of G7 ECSs to AZs where NVIDIA A40 GPUs are used
    • One NVENC (encoding) engine and two NVDEC (decoding) engines (including AV1 decoding) embedded
    +

    Supported Common Software

    +

    G7 ECSs are used in graphics acceleration scenarios, such as video rendering, cloud desktop, and 3D visualization. If the software relies on GPU DirectX and OpenGL hardware acceleration, use G7 ECSs. G7 ECSs support the following commonly used graphics processing software:

    +
    • AutoCAD
    • 3DS MAX
    • MAYA
    • Agisoft PhotoScan
    • ContextCapture
    • Adobe Premiere Pro
    • Solidworks
    • Unreal Engine
    • Blender
    • Vray
    +

    Notes

    +
    • G7 ECSs support the following OSs:
      • Windows Server 2019 Standard 64bit
      • Windows Server 2016 Standard 64bit
      • CentOS 8.2 64bit
      • CentOS 7.6 64bit
      • Ubuntu Server 20.04 64bit
      • Ubuntu Server 18.04 64bit
      +
    • G7 ECSs created using a public image have had the GRID driver of a specific version installed by default. However, you need to purchase and configure a GRID license by yourself. Ensure that the GRID driver version meets service requirements.

      For details about how to configure a GRID license, see Installing a GRID Driver on a GPU-accelerated ECS.

      +
    • If a G7 ECS is created using a private image, make sure that the GRID driver was installed during the private image creation. If the GRID driver has not been installed, install the driver for graphics acceleration after the ECS is created.

      For details about how to configure a GRID license, see Installing a GRID Driver on a GPU-accelerated ECS.

      +
    +

    Graphics-accelerated Enhancement G6

    Overview

    G6 ECSs use NVIDIA Tesla T4 GPUs to support DirectX, OpenGL, and Vulkan and provide 16 GiB of GPU memory. The theoretical Pixel rate is 101.8 Gpixel/s and Texture rate 254.4 GTexel/s, meeting professional graphics processing requirements.

    Select your desired GPU-accelerated ECS type and specifications.

    Specifications

    -
    Table 1 G6 ECS specifications

    Flavor

    +
    @@ -124,8 +212,8 @@

    G6 ECSs are used in graphics acceleration scenarios, such as video rendering, cloud desktop, and 3D visualization. If the software relies on GPU DirectX and OpenGL hardware acceleration, use G6 ECSs. G6 ECSs support the following commonly used graphics processing software:

    • AutoCAD
    • 3DS MAX
    • MAYA
    • Agisoft PhotoScan
    • ContextCapture

    Notes

    -
    • Table 2 lists the OSs supported by G6 ECSs. -
    Table 2 G6 ECS specifications

    Flavor

    vCPUs
    Table 2 Supported OS versions

    OS

    +
    • Table 3 lists the OSs supported by G6 ECSs. +
      @@ -152,7 +240,7 @@

      P3 ECSs use NVIDIA A100 GPUs and provide flexibility and ultra-high-performance computing. P3 ECSs have strengths in AI-based deep learning, scientific computing, Computational Fluid Dynamics (CFD), computing finance, seismic analysis, molecular modeling, and genomics. Theoretically, P3 ECSs provide 19.5 TFLOPS of FP32 single-precision performance and 156 TFLOPS (sparsity disabled) or 312 TFLOPS (sparsity enabled) of TF32 peak tensor performance.

      Specifications

      -
      Table 3 Supported OS versions

      OS

      Version
      Table 3 P3 ECS specifications

      Flavor

      +
      @@ -280,7 +368,7 @@

      P2s ECSs use NVIDIA Tesla V100 GPUs to provide flexibility, high-performance computing, and cost-effectiveness. P2s ECSs provide outstanding general computing capabilities and have strengths in AI-based deep learning, scientific computing, Computational Fluid Dynamics (CFD), computing finance, seismic analysis, molecular modeling, and genomics.

      Specifications

      -
      Table 4 P3 ECS specifications

      Flavor

      vCPUs
      Table 4 P2s ECS specifications

      Flavor

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      @@ -416,8 +504,8 @@

      Supported Common Software

      P2s ECSs are used in computing acceleration scenarios, such as deep learning training, inference, scientific computing, molecular modeling, and seismic analysis. If the software is required to support GPU CUDA, use P2s ECSs. P2s ECSs support the following commonly used software:
      • Common deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet
      • CUDA GPU rendering supported by RedShift for Autodesk 3dsMax and V-Ray for 3ds Max
      • Agisoft PhotoScan
      • MapD
      -
      Notes
      • Table 5 lists the OSs supported by P2s ECSs. -
      Table 5 P2s ECS specifications

      Flavor

      vCPUs
      Table 5 Supported OS versions

      OS

      +
      Notes
      • Table 6 lists the OSs supported by P2s ECSs. +
        @@ -458,7 +546,7 @@

        P2v ECSs use NVIDIA Tesla V100 GPUs and deliver high flexibility, high-performance computing, and high cost-effectiveness. These ECSs use GPU NVLink for direct communication between GPUs, improving data transmission efficiency. P2v ECSs provide outstanding general computing capabilities and have strengths in AI-based deep learning, scientific computing, Computational Fluid Dynamics (CFD), computing finance, seismic analysis, molecular modeling, and genomics.

        Specifications

        -
        Table 6 Supported OS versions

        OS

        Version
        Table 6 P2v ECS specifications

        Flavor

        +
        @@ -592,8 +680,8 @@

        Supported Common Software

        P2v ECSs are used in computing acceleration scenarios, such as deep learning training, inference, scientific computing, molecular modeling, and seismic analysis. If the software is required to support GPU CUDA, use P2v ECSs. P2v ECSs support the following commonly used software:
        • Common deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet
        • CUDA GPU rendering supported by RedShift for Autodesk 3dsMax and V-Ray for 3ds Max
        • Agisoft PhotoScan
        • MapD
        -
        Notes
        • Table 7 lists the OSs supported by P2v ECSs. -
        Table 7 P2v ECS specifications

        Flavor

        vCPUs
        Table 7 Supported OS versions

        OS

        +
        Notes
        • Table 8 lists the OSs supported by P2v ECSs. +
          @@ -633,7 +721,7 @@

          Computing-accelerated P2

          Overview

          Compared with P1 ECSs, P2 ECSs use NVIDIA Tesla V100 GPUs, which have improved both single- and double-precision computing capabilities by 50% and offer 112 TFLOPS of deep learning.

          Specifications -
          Table 8 Supported OS versions

          OS

          Version

          Table 8 P2 ECS specifications

          Flavor

          +
          @@ -748,8 +836,8 @@
          Notes
          • The system disk of a P2 ECS must be greater than or equal to 15 GiB. It is recommended that the system disk be greater than 40 GiB.
          • The local NVMe SSDs attached to P2 ECSs are dedicated for services with strict requirements on storage I/O performance, such as deep learning training and HPC. Local disks are attached to the ECSs of specified flavors and cannot be separately bought. In addition, you are not allowed to detach a local disk and then attach it to another ECS.

            Data may be lost on the local NVMe SSDs attached to P2 ECSs due to a fault, for example, due to a disk or host fault. Therefore, you are suggested to store only temporary data in local NVMe SSDs. If you store important data in such a disk, securely back up the data.

            -
          • P2 ECSs do not support specifications modification.
          • Table 9 lists the OSs supported by P2 ECSs. -
          Table 9 P2 ECS specifications

          Flavor

          vCPUs
          Table 9 Supported OS versions

          OS

          +
        • P2 ECSs do not support specifications modification.
        • Table 10 lists the OSs supported by P2 ECSs. +
          @@ -790,7 +878,7 @@

          P1 ECSs use NVIDIA Tesla P100 GPUs and provide flexibility, high performance, and cost-effectiveness. These ECSs support GPU Direct for direct communication between GPUs, improving data transmission efficiency. P1 ECSs provide outstanding general computing capabilities and have strengths in deep learning, graphic databases, high-performance databases, Computational Fluid Dynamics (CFD), computing finance, seismic analysis, molecular modeling, and genomics. They are designed for scientific computing.

          Specifications

          -
          Table 10 Supported OS versions

          OS

          Version
          Table 10 P1 ECS specifications

          Flavor

          +
          @@ -905,9 +993,8 @@
          • Deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet
          • RedShift for Autodesk 3dsMax, V-Ray for 3ds Max
          • Agisoft PhotoScan
          • MapD
          Notes
          • It is recommended that the system disk of a P1 ECS be greater than 40 GiB.
          • The local NVMe SSDs attached to P1 ECSs are dedicated for services with strict requirements on storage I/O performance, such as deep learning training and HPC. Local disks are attached to the ECSs of specified flavors and cannot be separately bought. In addition, you are not allowed to detach a local disk and then attach it to another ECS.

            Data may be lost on the local NVMe SSDs attached to P1 ECSs due to a fault, for example, due to a disk or host fault. Therefore, you are suggested to store only temporary data in local NVMe SSDs. If you store important data in such a disk, securely back up the data.

            -
          • After a P1 ECS is created, you must install the NVIDIA driver for computing acceleration. For details, see Installing a Tesla Driver and CUDA Toolkit on a GPU-accelerated ECS.
          • P1 ECSs do not support specifications modification.
          • P1 ECSs do not support automatic recovery.
            • If the host is faulty or subhealthy, you need to stop the ECS for hardware repair.
            • In case of system maintenance or hardware faults, the ECS will be redeployed (to ensure HA) and cold migrated to another host. The local disk data of the ECS will not be retained.
            -
          • Table 11 lists the OSs supported by P1 ECSs. -
          Table 11 P1 ECS specifications

          Flavor

          vCPUs
          Table 11 Supported OS versions

          OS

          +
        • After a P1 ECS is created, you must install the NVIDIA driver for computing acceleration. For details, see Installing a Tesla Driver and CUDA Toolkit on a GPU-accelerated ECS.
        • P1 ECSs do not support specifications modification.
        • Table 12 lists the OSs supported by P1 ECSs. +
          @@ -943,7 +1030,7 @@

          Pi2 ECSs use NVIDIA Tesla T4 GPUs dedicated for real-time AI inference. These ECSs use the T4 INT8 calculator for up to 130 TOPS of INT8 computing. The Pi2 ECSs can also be used for light-load training.

          Specifications

          -
          Table 12 Supported OS versions

          OS

          Version
          Table 12 Pi2 ECS specifications

          Flavor

          +
          @@ -1080,8 +1167,8 @@
          • After a Pi2 ECS is stopped, basic resources including vCPUs, memory, and images are not billed, but its system disk is billed based on the disk capacity. If other products, such as EVS disks, EIP, and bandwidth are associated with the ECS, these products are billed separately.

            Resources are released after a Pi2 ECS is stopped. If desired resources are insufficient when the Pi2 ECS is started after being stopped, starting the ECS might fail. Therefore, if you need to use a Pi2 ECS for a long time, keep the ECS running.

          -
          • Table 13 lists the OSs supported by Pi2 ECSs. -
          Table 13 Pi2 ECS specifications

          Flavor

          vCPUs

          Table 13 Supported OS versions

          OS

          +
          • Table 14 lists the OSs supported by Pi2 ECSs. +
            diff --git a/docs/ecs/umn/en-us_topic_0177512565.html b/docs/ecs/umn/en-us_topic_0177512565.html index b6c778a96..ad9a885df 100644 --- a/docs/ecs/umn/en-us_topic_0177512565.html +++ b/docs/ecs/umn/en-us_topic_0177512565.html @@ -2381,7 +2381,79 @@

            GPU-accelerated

            -
            Table 14 Supported OS versions

            OS

            Version
            Table 13 G6 ECS specifications

            Flavor

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            + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
            Table 13 G7 ECS specifications

            Flavor

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            vCPUs

            +

            Memory

            +

            (GiB)

            +

            Max./Assured Bandwidth

            +

            (Gbit/s)

            +

            Max. PPS

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            (10,000)

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            Max. NIC Queues

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            Max. NICs

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            GPUs

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            GPU Memory

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            (GiB)

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            Virtualization

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            g7.12xlarge.8

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            48

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            384

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            35/18

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            750

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            16

            +

            8

            +

            1 × NVIDIA-A40

            +

            1 × 48

            +

            KVM

            +

            g7.24xlarge.8

            +

            96

            +

            768

            +

            40/36

            +

            850

            +

            16

            +

            8

            +

            2 × NVIDIA-A40

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            2 × 48

            +

            KVM

            +
            +
            + +
            @@ -2480,7 +2552,7 @@
            Table 14 G6 ECS specifications

            Flavor

            vCPUs

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            Table 14 P3 ECS specifications

            Flavor

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            @@ -2593,7 +2665,7 @@
            Table 15 P3 ECS specifications

            Flavor

            vCPUs

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            Table 15 P2s ECS specifications

            Flavor

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            @@ -2722,7 +2794,7 @@
            Table 16 P2s ECS specifications

            Flavor

            vCPUs

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            Table 16 P2v ECS specifications

            Flavor

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            @@ -2848,7 +2920,7 @@
            Table 17 P2v ECS specifications

            Flavor

            vCPUs

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            Table 17 P2 ECS specifications

            Flavor

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            @@ -2953,7 +3025,7 @@
            Table 18 P2 ECS specifications

            Flavor

            vCPUs

            -
            Table 18 P1 ECS specifications

            Flavor

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            @@ -3059,7 +3131,7 @@
            Table 19 P1 ECS specifications

            Flavor

            vCPUs

            -
            Table 19 Pi2 ECS specifications

            Flavor

            +
            Table 20 Pi2 ECS specifications

            Flavor

            vCPUs