Supercomputers increase both computing power and available memory. This allows scientists to generate high resolution physics-based simulations. Most of these simulations produce a massive amount of data, resulting in potentially trillions of cells. Scientific visualization is an essential method for understanding this simulation data. Visualization algorithms are usually run on supercomputers to leverage additional memory and computational power. Running visualization algorithms in distributed memory settings is challenging for several reasons such as I/O cost, communication cost, load balancing, and coordination between the nodes. In this paper, we survey the challenges and techniques for visualizing large data sets on supercomputers, discussing different visualizing algorithms and analyzing the factors impacting the performance.