Comments (1)
Hi Luochao,
The reason they write it that way might be to keep the update thread safe. In my application, the global parameter can be updated while sharing with local workers or updated by many workers at the same time. You can try to add a lock to the updating step to maintain a sequential updating.
I have tried adding a lock, but from what I see, there is not too much improvement. I think without a lock, the updating mechanism would be like the mechanism in this paper "Hogwild: A lock-free approach to parallelizing stochastic gradient descent"
from pytorch-a3c.
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from pytorch-a3c.