In recent years, research achievements in deep learning have found widespread applications globally. To enhance the training efficiency of large-scale deep learning models, industry practices often involve constructing GPU clusters and configuring efficient task schedulers. However, deep learning training tasks exhibit complex performance characteristics such as performance heterogeneity and placement topological sensitivity. Scheduling without considering performance can lead to issues such as low resource utilization and poor training efficiency. In response to this challenge, a great number of schedulers of deep learning training tasks based on performance modeling have emerged. These schedulers, by constructing accurate performance models, delve into the intricate performance characteristics of tasks. Based on this understanding, they design more optimized scheduling algorithms, thereby forming more efficient scheduling solutions. This study begins with a modeling design perspective, providing a categorized review of the performance modeling methods employed by current schedulers. Subsequently, based on the optimized scheduling approaches from performance modeling by schedulers, a systematic analysis of existing task scheduling efforts is presented. Finally, this study outlines prospective research directions for performance modeling and scheduling in the future.