Additional SDKs for DOCA

Content:

NVIDIA® CUDA® is a parallel computing platform and programming model designed by NVIDIA for general-purpose GPU computing. The following steps detail how to set up CUDA on your environment.

To install CUDA on your converged accelerator:

1. Download and install the latest NVIDIA Data Center GPU driver.

2. Download and install CUDA.

   Info

   The CUDA version tested to work with DOCA SDK is 11.8.0.

   Info

   Downloading CUDA includes the latest NVIDIA Data Center GPU driver and CUDA toolkit. For more information about CUDA and driver compatibility, refer to the NVIDIA CUDA Toolkit Release Notes .

1.1. Configuring Operation Mode

There are two modes that the NVIDIA Converged Accelerator may operate in:

• Standard mode (default) – the BlueField and the GPU operate separately

• BlueField-X mode – the GPU is exposed to BlueField and is no longer visible on the host

To verify which mode the system is operating in, run:

            

            
host# sudo mst start
host# sudo mlxconfig -d <device-id> q PCI_DOWNSTREAM_PORT_OWNER[4]
        

• Standard mode output:

  Copy

  Copied!

              
  
              
  Device #1:
  […]
  Configurations:                              Next Boot
           PCI_DOWNSTREAM_PORT_OWNER[4]        DEVICE_DEFAULT(0)
          

  To configure standard mode, run:

  Copy

  Copied!

              
  
              
  host# mlxconfig -d <device-id> s PCI_DOWNSTREAM_PORT_OWNER[4]=0x0
          

• BlueField-X mode output:

  Copy

  Copied!

              
  
              
  Device #1:
  […]
  Configurations:                              Next Boot
           PCI_DOWNSTREAM_PORT_OWNER[4]        EMBEDDED_CPU(15)
          

  To configure BlueField-X mode, run:

  Copy

  Copied!

              
  
              
  host# mlxconfig -d <device-id> s PCI_DOWNSTREAM_PORT_OWNER[4]=0xF
          

            

            
host# ipmitool power cycle
        

1.2 Downloading and Installing CUDA Toolkit and Driver

This section details the necessary steps to set up CUDA on your environment. It assumes that a BFB image has already been installed on your environment.

1. Install CUDA by visiting the CUDA Toolkit Downloads webpage.

   Info

   Select the Linux distribution and version relevant for your environment.

   Info

   This section shows the native compilation option either on x86 or aarch64 hosts.

2. Test that the driver installation completed successfully. Run:

   Copy

   Copied!

               
   
               
   dpu# nvidia-smi
    
   Tue Apr  5 13:37:59 2022       
   +-----------------------------------------------------------------------------+
   | NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.8     |
   |-------------------------------+----------------------+----------------------+
   | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
   | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
   |                               |                      |               MIG M. |
   |===============================+======================+======================|
   |   0  NVIDIA BF A10       Off  | 00000000:06:00.0 Off |                    0 |
   |  0%   43C    P0    N/A / 225W |      0MiB / 23028MiB |      0%      Default |
   |                               |                      |                  N/A |
   +-------------------------------+----------------------+----------------------+
   +-----------------------------------------------------------------------------+
   | Processes:                                                                  |
   |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
   |        ID   ID                                                   Usage      |
   |=============================================================================|
   |  No running processes found                                                 |
   +-----------------------------------------------------------------------------+
           

3. Verify that the installation completed successfully.

   1. Download CUDA samples repo. Run:

      Copy

      Copied!

                  
      
                  
      dpu# git clone https://github.com/NVIDIA/cuda-samples.git
              

   2. Build and run vectorAdd CUDA sample. Run:

      Copy

      Copied!

                  
      
                  
      dpu# cd cuda-samples/Samples/0_Introduction/vectorAdd
      dpu# make
      dpu# ./vectorAdd
              

      Note

      If the vectorAdd sample works as expected, it should output "Test Passed".

      Note

      If it seems that the GPU is slow or stuck, stop execution and run:

      Copy

      Copied!

                  
      
                  
      dpu# sudo setpci -v -d ::0302 800.L=201 # CPL_VC0 = 32
              

1.3 GPUDirect RDMA

For information on GPUDirect RMDA and more, refer to DOCA GPUNetIO documentation.

NVIDIA Rivermax offers a unique IP-based solution for any media and data streaming use case.

This section provides the steps to install Rivermax assuming that a BFB image has already been installed on your environment.

2.1 Downloading Rivermax Driver

1. Navigate to the NVIDIA Rivermax SDK product page.

2. Register to be able to download the driver package using the JOIN button at the top of the page.

3. D ownload the appropriate driver package according to your BFB under the "Linux" subsection. For example, for Ubuntu 22.04 BFB, download rivermax_ubuntu2204_<version>.tar.gz.

2.2 Installing Rivermax Driver

1. Copy the .tgz file to BlueField:

   Copy

   Copied!

               
   
               
   host# sudo scp -r rivermax_ubuntu2204_<version>.tar.gz ubuntu@192.168.100.2:/tmp/
           

2. Extract the Rivermax file:

   Copy

   Copied!

               
   
               
   dpu# sudo tar xzf rivermax_ubuntu2204_<version>.tar.gz
           

3. Install the Rivermax driver package:

   Copy

   Copied!

               
   
               
   dpu# cd <rivermax-version>/Ubuntu.22.04/deb-dist/aarch64/
   dpu# sudo dpkg -i rivermax_<version>.deb
           

2.3 Installing Rivermax Libraries from DOCA

Rivermax libraries are compatibles with DOCA components and can be found inside the doca-dpu-repo.

1. Unpack the doca-dpu-repo:

   Copy

   Copied!

               
   
               
   dpu# sudo dpkg -i doca-dpu-repo-ubuntu2204-local_<version>_arm64.deb
           

2. Run apt update:

   Copy

   Copied!

               
   
               
   dpu# sudo apt-get update
           

3. Install the Rivermax libraries:

   Copy

   Copied!

               
   
               
   dpu# sudo apt install doca-sdk-rmax
   dpu# sudo apt install libdoca-sdk-rmax-dev
           

For additional details and guidelines, please visit the NVIDIA Rivermax SDK product page.