CUDA usage

Starting from FW 1.4.30 all 3D data is processed by default on the GPU using a CUDA based implementation.

useCuda parameter

Variable name	Short description
useCuda	Defines whether to process the data on the GPU (useCuda = true) or CPU (useCuda = false).

When useCuda is True, the distance data is computed on the GPU. This enables more camera streams and applications to be processed simultaneously on the total system - including CPU and GPU. We typically recommend to leave CUDA processing enabled.

Processing the 3D data using CUDA can be enabled or disabled within the system JSON configuration, by changing the <value> placeholder accordingly:

{
  "ports": {
    "portX": {
      "processing": {
        "diParam": {
          "useCuda": <value>
        }
      }
    }
  }
}

Note

• Note that using CUDA will lead to a small increase of the RAM usage per port: see the resource management documentation for more details.

• Note that disabling useCuda does not automatically free up the RAM previously allocated to the CUDA processes. To free up the RAM, a reboot is necessary. Check out the RAM usage section below.

RAM usage

The process to free up RAM should be as follows:

1. Set the useCuda parameter to false for all the desired ports. A mix and match is possible.

{
  "ports": {
    "portX": {
      "processing": {
        "diParam": {
          "useCuda": false
        }
      }
    }
  }
}

For example in Python with:

o3r.set({"ports":{"portX":{"processing":{"diParam":{"useCuda":False}}}}})

Replace the placeholder value portX accordingly.

2. Perform a save_init for these specific parameters, for example in Python with:

o3r.save_init(["/ports/portX/processing/diParam/useCuda"])

where portX is the relevant port.

If multiple camera streams need to be persistently configured to run on the CPU, all the streams and their respective parameters must be set in a single save_init command. Setting multiple save_init commands consecutively will only apply the last one.

You can simply use the save_init method on the list of parameters, for example in Python with:

o3r.save_init(["/ports/portX/processing/diParam/useCuda", "/ports/portY/processing/diParam/useCuda"])

where portX and PortY are the relevant ports.

3. Perform a reboot to release previously allocated RAM resources. The system will reboot with the saved configuration.

Note

If the user performs a save_init command without providing the respective JSON pointers, the complete JSON configuration will be saved, including the complete hardware configuration. This means that exchanging cameras, for example, will raise an error.

To remove this persistent configuration, for example in case a camera head needs to be replaced, one has to perform a factory_reset.

Removing the persistent configuration is currently only via a factory_reset possible.

Required concurrent parameter changes

Since useCuda is not a sticky parameter, changing a parameter higher in the JSON hierarchy will result in all corresponding parameters below that JSON level being reset to their defaults unless changed within the same set command. Therefore one must change the useCuda parameter always when changing the selected camera mode, for example standard_range2m to standard_range4m and vice versa.

For example, when changing the mode of portX to standard_range2m, change the value of useCuda in the same call. In Python, you can do this with:

o3r.set({"ports":{"portX":{"mode":"standard_range2m", "processing":{"diParam":{"useCuda":False}}}}})

where portX is the relevant port.