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Solution Architecture

In Composable AI solutions, the GPUs are in shared resource pools (GPU chassis) and disaggregated from the physical servers by using a PCIe fabric. GPUs can be assigned or reassigned to any connected servers to rapidly meet the needs of variable AI applications or different AI development stages. Users can provision GPUs from a pool of GPU resources using the management GUI or API.

  • Users
  • Applications
  • Guest OS
  • VM
  • Host OS+Hypervisor
  • Composed Server
  • User
    User
    User
    User
  • vmware
    • cpux8
    • memoryx8
    • gpux8

    V_Server1

  • User
  • ubuntu
    • cpux1
    • memoryx4
    • gpux4

    V_Server2

  • ...
  • User
    User
    User
  • Red Hat
    • cpux2
    • memoryx6
    • gpux6

    V_ServerN

Virtual view

Physical view

  • Physical Server
  • PCIe Fabric
    PCIe Virtualization layer
  • GPU Chassis
  • P_Server1

    • cpu
    • memory
    • gpu
    • gpu
    • gpu
    • gpu
  • P_Server2

    • cpu
    • memory
    • gpu
    • gpu
    • gpu
  • ...
  • P_ServerN

    • cpu
    • memory
    • gpu
    • gpu
  • gpu gpu gpu gpu gpu gpu gpu gpu
    gpu gpu gpu gpu gpu gpu gpu gpu
    gpu gpu gpu gpu gpu gpu gpu gpu
    gpu gpu gpu gpu gpu gpu gpu gpu

    GPU Pool

    Virtualized GPU usage

    On the orange area, the GPU is used in a traditional virtualized environment. It is also shared across multiple applications or users. The GPU virtualization solution is widely adapted in the existing infrastructure.

    • Users
    • Applications
    • Guest OS
    • VM
    • Host OS+Hypervisor
    • Composed Server
    • User
      User
      User
      User
    • vmware
      • cpux8
      • memoryx8
      • gpux8

      V_Server1

    • User
    • ubuntu
      • cpux1
      • memoryx4
      • gpux4

      V_Server2

    A disaggregated approach for GPU composable infrastructure by using a PCIe fabric

    On the blue area, four key components are in a composable AI solution and these are the following: physical server, GPU chassis (GPU pool), PCIe fabric, and the management software. GPUs in GPU chassis are dynamically assigned to any connected physical server (through PCIe fabric) by management GUI or API to build up a composed server. GPU virtualization and composable AI solutions are complementary to each other.

    • Physical Server
    • PCIe Fabric
      PCIe Virtualization layer
    • GPU Chassis
  • P_Server1

    • cpu
    • memory
    • gpu
    • gpu
    • gpu
    • gpu
  • P_Server2

    • cpu
    • memory
    • gpu
    • gpu
    • gpu
  • gpu gpu gpu gpu gpu gpu gpu gpu
    gpu gpu gpu gpu gpu gpu gpu gpu
    gpu gpu gpu gpu gpu gpu gpu gpu

    GPU Pool

    The combination of composable infrastructure and traditional virtualization architecture

    GPUs are installed in a GPU chassis. The host adapter (PCIe Gen4 x16) is installed in the PCIe slot of the physical server. A GPU chassis is connected to 2-4 physical servers via cables running the PCIe protocol.

    Users can use the management software of a single unit or H3 management center to assign (un-assign) a GPU to (from) a connected server.

    Physical Server A

    • cpu
    • memory
    • Host Adapter
      gpu x40

    PCIe cable
    ( PCIe 4.0 )

    • gpu
    • gpu
    • gpu
    • gpu

    Physical Server A

    • cpu
    • memory
    • Host Adapter
      gpu x02
    • gpu
    • gpu
    gpu gpu
    gpu gpu
    gpu gpu
    gpu gpu
    gpu gpu
    gpu gpu
    gpu gpu
    gpu gpu

    GPU Chassis

    Composed server

    The composed server is composed by using a physical server and a GPU chassis. For example, if 2 GPUs in a GPU chassis are assigned to the first connected physical server, the composed server is a 2 GPU server (physical server plug 2 GPUs). These two GPUs are in a composed server work as direct-attached GPUs.

    Virtual view

    Composed Server

    • cpu
    • memory
    • gpu x2
    Physical view

    Physical Server

    • cpu
    • memory
    • Host Adapter
      gpu x2

    PCIe cable
    ( PCIe 4.0 )

    • gpu
    • gpu
    gpu gpu
    gpu gpu
    gpu gpu
    gpu gpu
    gpu gpu
    gpu gpu
    gpu gpu
    gpu gpu

    GPU Chassis

    Composable GPU

    Physical Server

    • cpu
    • memory
    • gpu x2

    GPU server

    Traditional GPU virtualization architecture

    Users can use GPUs in composed servers as normal GPUs. In AI or HPC, GPUs are dedicated to one VM as bare metal GPU servers. In a virtual desktop or development environment, the GPU is shared across multiple VMs. This part is exactly the same as which GPUs are consumed in the existing infrastructure.

    • Users
    • Applications
    • Guest OS
    • VM
    • Host OS+Hypervisor
    • Composed Server
    • User
      User
      User
      User
    • vmware
      • cpux8
      • memoryx8
      • gpux8

      V_Server1

    • User
    • ubuntu
      • cpux1
      • memoryx4
      • gpux4

      V_Server2

    IT administrator provisions GPU resource and checks the analytic information

    • Management Software UI
    • Analytic Information UI
    • Management Software

    • Analytic Information

    • Through a graphic user interface, H3 Center enables GPU provisioning as well as GPU chassis discovery, inventory, port configuration, diagnostics, monitoring, fault detection, utilization auditing, and performance.

      The IT administrator can provision or redeploy GPU and configure PCIe ports in just a few seconds without service interruptions.

    • H3 Center discovers the GPU utilization and performance then performs continuous real-time analysis to the administrator for better resource utilization.

      H3 Center gives IT professionals the insight and control they need to manage and mitigate issues to anticipate failure risks. It also presents actions the administrator can take to anticipate and avoid problems.

    Falcon 4010
    Falcon 4016

    Falcon 4010 and Falcon 4016

    Dynamically assign, move, and scale GPU pools with greater flexibility and efficiency to deliver optimal value. Falcon 4016 and Falcon 4010 are designed to support your AI, HPC, and big data projects. You can tailor GPU configurations to your own requirements. This on-the-fly hardware capacity helps reduce stranded assets and over provisioning while greatly improving performance and efficiency.

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