SELECTION OF RUNTIME PERFORMANCE ESTIMATOR USING MACHINE LEARNING

Systems and techniques are provided for selecting a runtime performance estimator. An example method includes receiving, by a machine learning model, at least one compute workload and a target hardware parameter, wherein the target hardware parameter identifies one or more hardware components configurable to execute the at least one compute workload; identifying a plurality of runtime performance estimators for obtaining a predicted performance of the at least one compute workload on the one or more hardware components; determining a plurality of accuracy parameters and a plurality of cost parameters that are associated with the predicted performance obtained from the plurality of runtime performance estimators; and selecting, based on the plurality of accuracy parameters and the plurality of cost parameters, a preferred runtime performance estimator from the plurality of runtime performance estimators for obtaining the predicted performance of the at least one compute workload using the one or more hardware components.