The　recent　breakthroughs　in　large-scale　DNN　attract　significant　attention　from　both　academia　and　industry　toward　distributed　DNN　training　techniques.　Due　to　the　time-consuming　and　expensive　execution　process　of　large-scale　distributed　DNN　training,　it　is　crucial　to　model　and　predict　the　performance　of　distributed　DNN　training　before　its　actual　deployment,　in　order　to　optimize　the　design　of　distributed　DNN　training　at　low　cost.　This　paper　analyzes　and　emphasizes　the　importance　of　modeling　and　predicting　the　performance　of　distributed　DNN　training,　categorizes　and　analyses　the　related　state-of-the-art　works,　and　discusses　future　challenges　and　opportunities　for　this　research　field.　The　objectives　of　this　paper　are　twofold:　first,　to　assist　researchers　in　understanding　and　choosing　suitable　modeling　and　prediction　tools　for　large-scale　distributed　DNN　training,　and　second,　to　encourage　researchers　to　propose　more　valuable　research　about　performance　modeling　and　prediction　for　distributed　DNN　training　in　the　future.