Interior-point methods (IPMs) for semidefinite optimization (SDO) have been studied intensively, due to their polynomial complexity and practical efficiency. Recently, Peng et al. [11, 12] introduced so-called self-regular kernel (and barrier) functions and designed primal-dual interior-point algorithms based on self-regular proximity for linear optimization (LO) problems. They have also extended the approach for LO to SDO. In this paper, we present numerical results of primal-dual interior-point algorithm for SDO problems based on kernel functions. We compare the iteration numbers of the kernel functions  in [3],  in [15] and  in [13] while using large-update method. It is found that in most cases the kernel function  can produce better iteration numbers than the kernel functions  and

semidefinite optimization, interior-point algorithm, kernel function, large-update method, numerical results.