Generating samples from a high dimensional probability distribution is a fundamental task with wide-ranging applications in the area of scientific computing, statistics and machine learning. This talk will revisit some existing sampling algorithms based on time discretizations of stochastic differential equations (SDEs). New error bounds will be then provided for the considered sampling algorithms without log-concavity. Also, numerical experiments will be presented to corroborate the theoretical findings.