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For parallel processing:
there is only currently a limited amount of code in Autoware.auto that requires parallel acceleration. Previously OpenMP has been used https://gitlab.com/autowarefoundation/autoware.auto/AutowareAuto/-/commit/8d18dbea27c48445c12b1fa54de372bf2f99e31a. There has not been a need for massive parallel acceleration in Autoware just yet. In the future when a need arises, we will choose an appropriate API to use.
Avoiding CUDA is deliberate because it is a proprietary API with vendor hardware lock-in. This is highly undesirable in an open-source project such as Autoware.
For Machine Learning:
We are currently working on adding machine learning facilities in Autoware via TVM https://gitlab.com/autowarefoundation/autoware.auto/AutowareAuto/-/issues/721. This allows the abstraction of backends. We have chosen libvulkan for our reference implementation. This means there is no vendor lockin for hardware acceleration.
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