A method for assisting in the maintenance and optimization of a supercomputer, comprising: - a step of sending, by at least one sensor (C1, C2, Cn), a signal representative of statistical data of at least one calculation node (N1, N2, Nn) of the supercomputer to a maintenance assistance system; - a step of predicting, at regular intervals, by a prediction algorithm managed by a processor of the maintenance assistance system, future variations of the statistical data from the signals representative of the statistical data that are sent by the sensor(s) (C1, C2, Cn) and stored in storage means of the maintenance assistance system; - a step of detecting in real time, by a detection algorithm managed by the processor, anomalies of variations in the signals representative of the statistical data that are sent by the sensor(s) (C1, C2, Cn) relative to the future variations predicted in the prediction step; the method being characterized in that the steps of predicting future variations and detecting anomalies comprise at least a first and a second filtering, and respectively, of said signals representative of the statistical data on the basis of said sensor(s) having sent said signals that are necessary for the implementation of the maintenance and the optimization of said supercomputer, and in that it comprises a step of selecting, for each sensor (C1, C2, Cn), by an interface of the computing infrastructure, a type of signal necessary for the prediction and/or detection of anomalies, and selecting, in the set of sensors (C1, C2, Cn), a particular number of sensors (C1, C2, Cn) which will be used for filtering said data or said signals necessary for the prediction and/or detection of anomalies. - a step of sending, by at least one sensor (C1, C2, Cn), a signal representative of statistical data of at least one calculation node (N1, N2, Nn) of the supercomputer to a maintenance assistance system; - a step of predicting, at regular intervals, by a prediction algorithm managed by a processor of the maintenance assistance system, future variations of the statistical data from the signals representative of the statistical data that are sent by the sensor(s) (C1, C2, Cn) and stored in storage means of the maintenance assistance system; - a step of detecting in real time, by a detection algorithm managed by the processor, anomalies of variations in the signals representative of the statistical data that are sent by the sensor(s) (C1, C2, Cn) relative to the future variations predicted in the prediction step; the method being characterized in that the steps of predicting future variations and detecting anomalies comprise at least a first and a second filtering, and respectively, of said signals representative of the statistical data on the basis of said sensor(s) having sent said signals that are necessary for the implementation of the maintenance and the optimization of said supercomputer, and in that it comprises a step of selecting, for each sensor (C1, C2, Cn), by an interface of the computing infrastructure, a type of signal necessary for the prediction and/or detection of anomalies, and selecting, in the set of sensors (C1, C2, Cn), a particular number of sensors (C1, C2, Cn) which will be used for filtering said data or said signals necessary for the prediction and/or detection of anomalies. the method being characterized in that the steps of predicting future variations and detecting anomalies comprise at least a first and a second filtering, and respectively, of said signals representative of the statistical data on the basis of said sensor(s) having sent said signals that are necessary for the implementation of the maintenance and the optimization of said supercomputer, and in that it comprises a step of selecting, for each sensor (C1, C2, Cn), by an interface of the computing infrastructure, a type of signal necessary for the prediction and/or detection of anomalies, and selecting, in the set of sensors (C1, C2, Cn), a particular number of sensors (C1, C2, Cn) which will be used for filtering said data or said signals necessary for the prediction and/or detection of anomalies.