As computational scientists are confronted with increasingly massive datasets from supercomputing simulations and experiments, one of the biggest challenges is having the right tools to gain scientific insight from the data. One common method for gaining insight is to use scientific visualization, which transforms abstract data into more readily comprehensible images using advanced computer software and computer graphics. But the ever-growing size of scientific datasets presents a significant challenge to modern scientific visualization tools. As a result, there is a great deal of motivation to explore use of large, parallel resources, such as those at the U.S. Department of Energy's (DOE) supercomputing centers, to take advantage of their vast computational processing power, I/O bandwidth and large memory footprint.