Extend HW image decoder bench script to support multiple GPUs

[JanuszL]

• adds an ability to run multiple data processing pipelines on
  multiple GPUs inside HW image decoder bench script

Category:

Other (e.g. Documentation, Tests, Configuration)

Description:

• adds an ability to run multiple data processing pipelines on
  multiple GPUs inside HW image decoder bench script

Additional information:

Affected modules and functionalities:

• tools/hw_decoder_bench.py

Key points relevant for the review:

• NA

Tests:

• Existing tests apply
• New tests added
  • Python tests
  • GTests
  • Benchmark
  • Other
• N/A

Checklist

Documentation

• Existing documentation applies
• Documentation updated
  • Docstring
  • Doxygen
  • RST
  • Jupyter
  • Other
• N/A

DALI team only

Requirements

• Implements new requirements
• Affects existing requirements
• N/A

REQ IDs: N/A

JIRA TASK: N/A

[JanuszL]

!build

[dali-automaton]

CI MESSAGE: [10021551]: BUILD STARTED

[szalpal]

Added a tiny suggestion, LGTM regardless.

[szalpal]

I recall some issue that device_ids do not have to be consecutive (I might be wrong though). How about providing a list of devices instead? Something along the lines:

Suggested change

	parser.add_argument('-n', dest='gpu_num',
	help='Number of GPUs used starting from device_id', default=1, type=int)
	parser.add_argument('-d', dest='device_id', help='device_id', default=0, nargs='*')

And the usage would be:

python hw_decoder_bench.py -d 1 3 -b ...
python hw_decoder_bench.py -d 0 -b ...

    for di in range(args.device_id):
        pipes.append(DecoderPipeline(device_id=di))

[JanuszL]

The numbers are consecutive, but the order may differ from the PCI order.
I rather imagine this script to be used to test on heterogenous systems and the user will just decide how many GPUs is willing to use.

[dali-automaton]

CI MESSAGE: [10021551]: BUILD PASSED