Method for optimizing the execution of high-performance computing workflows being sequences of operations intended to be executed on a high-performance computing system, the high-performance computing system comprising at least one file cache subsystem associated with a filling state, and a storage subsystem, each workflow being associated with at least one file stored in the storage subsystem, on which the workflow performs operations, characterized in that the method comprises the following steps: - For each workflow intended to be executed: ∘For each file associated with the workflow: • Collection of first features relative to the file and/or to the operations the workflow performs on the file, to obtain a signature for the file; ∘If a condition according to which the workflow is not associated with a workflow lifecycle model is verified: • For each file associated with the workflow: ▪ Collection of second features relative to the access operations the workflow performs on the file, to obtain a file lifecycle corresponding to the sequence of operations performed by the workflow on the file; • Building and supervised training of a workflow lifecycle model on the signature and the file lifecycle obtained for each file associated with the workflow, to predict a file lifecycle from the corresponding signature, and thus obtain a workflow lifecycle comprising each predicted file lifecycle; • Association of the trained workflow lifecycle model with the workflow; ∘Use of the trained workflow lifecycle model associated with the workflow to obtain a workflow lifecycle for the workflow; ∘Use of an optimization model to obtain from the obtained workflow lifecycle and the filling state of each file cache subsystem, policies for placement in at least one file cache subsystem, of each file associated with the workflow; each file associated with the workflow being placed in at least one file cache subsystem according to the obtained policies, for the execution of the workflow. - For each workflow intended to be executed: ∘For each file associated with the workflow: • Collection of first features relative to the file and/or to the operations the workflow performs on the file, to obtain a signature for the file; ∘If a condition according to which the workflow is not associated with a workflow lifecycle model is verified: • For each file associated with the workflow: ▪ Collection of second features relative to the access operations the workflow performs on the file, to obtain a file lifecycle corresponding to the sequence of operations performed by the workflow on the file; • Building and supervised training of a workflow lifecycle model on the signature and the file lifecycle obtained for each file associated with the workflow, to predict a file lifecycle from the corresponding signature, and thus obtain a workflow lifecycle comprising each predicted file lifecycle; • Association of the trained workflow lifecycle model with the workflow; ∘Use of the trained workflow lifecycle model associated with the workflow to obtain a workflow lifecycle for the workflow; ∘Use of an optimization model to obtain from the obtained workflow lifecycle and the filling state of each file cache subsystem, policies for placement in at least one file cache subsystem, of each file associated with the workflow; each file associated with the workflow being placed in at least one file cache subsystem according to the obtained policies, for the execution of the workflow. ∘For each file associated with the workflow: • Collection of first features relative to the file and/or to the operations the workflow performs on the file, to obtain a signature for the file; • Collection of first features relative to the file and/or to the operations the workflow performs on the file, to obtain a signature for the file; ∘If a condition according to which the workflow is not associated with a workflow lifecycle model is verified: • For each file associated with the workflow: ▪ Collection of second features relative to the access operations the workflow performs on the file, to obtain a file lifecycle corresponding to the sequence of operations performed by the workflow on the file; • Building and supervised training of a workflow lifecycle model on the signature and the file lifecycle obtained for each file associated with the workflow, to predict a file lifecycle from the corresponding signature, and thus obtain a workflow lifecycle comprising each predicted file lifecycle; • Association of the trained workflow lifecycle model with the workflow; • For each file associated with the workflow: ▪ Collection of second features relative to the access operations the workflow performs on the file, to obtain a file lifecycle corresponding to the sequence of operations performed by the workflow on the file; • Building and supervised training of a workflow lifecycle model on the signature and the file lifecycle obtained for each file associated with the workflow, to predict a file lifecycle from the corresponding signature, and thus obtain a workflow lifecycle comprising each predicted file lifecycle; • Association of the trained workflow lifecycle model with the workflow; ∘Use of the trained workflow lifecycle model associated with the workflow to obtain a workflow lifecycle for the workflow; ∘Use of an optimization model to obtain from the obtained workflow lifecycle and the filling state of each file cache subsystem, policies for placement in at least one file cache subsystem, of each file associated with the workflow;