Have you done any multi-gpu training? Generally every GPU gets a process. Coordinating between them and passing around data between them is complex and can easily have performance issues since normal communication between python processes requires some sort of serialization/de-serialization of objects (there are many * here when it comes to GPU training). This has the potential to simplify all of that and remove a lot of inter-process communication which is just pure overhead.

Of course ML will benefit from it. Soon you will be able to run your dataloaders/data preprocessing in different threads which will not starve your GPUs of data.

If you have done ML with PyTorch or Tensorflow you will know how much multithreading can improve data loading performance. Currently multiprocessing provides the necessary parallelization of data loading but it is painful and riddle with bugs.