The present application relates to the technical field of computing power scheduling, in particular to a dynamic computing power scheduling method and
system for AI training tasks, which realizes the quantification of node thermal risk by obtaining the
thermal state pressure index through the weighted calculation of thermal margin and temperature rise trend; obtains the static
leakage power consumption from the total
power consumption by stripping the dynamic
power consumption, obtains the electrical state
pressure index by combining the leakage
temperature coefficient, and accurately identifies the risk of node thermal-electric positive and
negative feedback instability; captures the implicit collapse problem of long-term performance of the node by calculating the deviation accumulation state through the exponential
moving average; calculates the expected
cooling time based on the
heat capacity, cooling heat dissipation rate and idle
power consumption, locks the scheduling state through the time lock mechanism, and offsets the influence caused by the
inertia of the cooling
system; realizes multi-dimensional risk warning, and the warning dimension is more comprehensive and the prediction accuracy is higher; calculates the computing
power loss factor through the thermal and electrical
pressure index, corrects the nominal computing power to obtain the effective available
throughput, so that the scheduling and distribution of the AI training operator are more suitable for the real running state of the node.