Many scientific workflows are composed of fine computational granularity tasks, yet they are composed of thousands of them and are data intensive in nature, thus requiring resources such as the TeraGrid to execute efficiently. In order to improve the performance of such applications, we often employ task clustering techniques to increase the computational granularity of workflow tasks. The goal is to minimize the completion time of the workflow by reducing the impact of queue wait times. In this paper, we examine the performance impact of the clustering techniques using the Pegasus workflow management system. Experiments performed using an astronomy workflow on the NCSA TeraGrid cluster show that clustering can achieve a significant reduction in the workflow completion time (up to 97%).