Abstract: A method for scheduling computing tasks on a supercomputer including offline reinforcement learning (OFRL) of a scheduler on a database (LDB). The database includes at least one execution history (HIST) that includes the state (LHPCS) of a learning supercomputer at several moments (T, T?1); the actions (LACT) related to the scheduling of learning tasks on the learning supercomputer at those moments (T); and a reward (REW) related to each task. The method also includes the use of the scheduler trained on the computing tasks to be scheduled.

Abstract: A method of representing a distributed computing system, the distributed computing system comprising a plurality of processing devices connected together according to a predefined topology. The method comprising receiving at least one piece of data from an activity log file relating to at least one processing device among the plurality of processing devices, receiving at least one metric relating to at least one processing device among the plurality of processing devices, receiving at least the predefined topology of the distributed computing system, constructing a graph representative of a distributed computing system operation, the graph comprising the data item extracted from the received log file, the received metric, and the received topology, and embedding at least one part of the graph to obtain at least one state vector representing the at least one part of the embedded graph.