Comments (2)
from explicit_hybrid_mpc.
Possibly fixed this issue by using a MAX_ASYNC_SEND
variable and implementing the relevant functionality in tools.py:MPICommunicator:send() which waits until scheduler reads the worker's messages once the buffer of messages is full up to MAX_ASYNC_SEND
. This makes sure that at most MAX_ASYNC_SEND*(MPI.COMM_WORLD.Get_size()-1)
messages are ever in the MPI message pipe, which will hopefully be not too much and prevent an overflow of messages that (I think) is what is causing the above issue.
from explicit_hybrid_mpc.
Related Issues (2)
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