Discover and quantify the performance gains of dynamic parallelism for clustering algorithms on GPUs. Dynamic parallelism effectively eliminates the superfluous back and forth communication between the GPU and CPU through nested kernel computations. The change in performance is measured using two well-known clustering algorithms that exhibit data dependencies: the K-means clustering and the hierarchical clustering. K-means has a sequential data dependence wherein iterations occur in a linear fashion, while the hierarchical clustering has a tree-like dependence that produces split tasks. Analyzing the performance of these data-dependent algorithms gives us a better understanding of the benefits or potential drawbacks of CUDA 5's new dynamic parallelism feature.