Abstract: Integrated Singular Value Decomposition (iSVD) is a randomized method proposed by T.Chen et.al. iSVD is carefully designed to obtain high parallelism with respect to accuracy and execution speed. In this project, we evaluate the iSVD algorithm with several supercomputers provided in JHPCN. Adaptation of auto-tuning to iSVD and other applications will also discussed.