Data processing method and apparatus, electronic device, and storage medium

Abstract

Embodiments of this disclosure include a data processing method. In the method, a training sample set that includes a plurality of graph computing task training samples is obtained. At least one performance indicator feature of each of the graph computing task training samples is extracted. The at least one performance indicator feature includes one or more of a graph data feature, a graph processing platform feature, a graph algorithm feature, and a machine hardware feature. A target performance prediction model is generated based on a mapping relationship between actual execution times of the graph computing task training samples and the performance indicator features. According to at least one performance indicator feature of an inputted graph computing task test sample, a predicted execution time of the graph computing task test sample is output based on the target performance prediction model.