MPI (Moldflow Plastics Insight) information scheduling method based on reinforcement learning under multi-network environment
A multi-network environment and reinforcement learning technology, applied in the field of message scheduling, can solve problems such as unbalanced communication load distribution, poor adaptability to computing environments, and inability to adapt to dynamic changes in network status.
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[0088] figure 1 It is a flowchart of the round-robin scheduling method in the background technology. The specific implementation is as follows:
[0089] The first step, initialization: set the identification i=0 of the sending network, set the value of the maximum length seg_max of the message segment, set and start the sending timer timer;
[0090] The second step, waiting for the message sending request of the parallel application program: if there is an unprocessed message sending request, take out the message request that arrives first, obtain the MPI message that the request needs to send, and set the scheduling end flag FINISHED=0, turn to the first step Three steps, otherwise continue to wait;
[0091] The third step is to obtain the current message segment according to the value of seg_max: if the MPI message has been fully scheduled, that is, FINISHED=1, then go to the second step; Take a piece of content with a length of seg_max from the part in order from front ...
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