Patent ID: 11934870
Assignee: LE COMMISSARIAT À L'ÉNERGIE ATOMIQUE ET AUX ÉNERGIES ALTERNATIVES
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 12:
13. A computing environment configured to implement a method for scheduling a set of computing tasks stored in a queue of a supercomputer, the scheduling comprising planning an execution of each computing task of the set of computing tasks by the supercomputer and allocating at least one computing resource of the supercomputer to the execution of each computing task of the set of computing tasks, the method comprising
offline reinforcement learning of a scheduler on a training database to obtain a trained scheduler capable of scheduling the set of computing tasks, the training database comprising at least one execution history, each execution history of the at least one execution history being associated with a training supercomputer and comprising, at each given moment of a time interval
a state of a queue of a learning supercomputer at the each given moment, said queue storing a set of learning computing tasks;
a state of the learning supercomputer at the each given moment;
each action relating to the scheduling of the set of learning computing tasks performed at the each given moment, said scheduling being implemented by a learning scheduler based on the state of the queue of the learning supercomputer and on the state of the learning supercomputer at a moment preceding the each given moment;
a reward related to said each action, each reward of said reward related to said each action being calculated based on the state of the queue of the learning supercomputer and on the state of the learning supercomputer at the each given moment and at the moment preceding the each given moment;

use of the scheduler trained on the set of computing tasks stored in the queue of the supercomputer,, the computing environment comprising:, a computing module configured to perform the offline reinforcement learning of the scheduler on the training database;, at least one storage module configured to store the training database; and, the supercomputer having the queue on which the set of computing tasks to be scheduled is stored, configured to use the scheduler trained by the computing module on the set of computing tasks to be scheduled.