ABSTRACT
Parallelization is the most common way to deal with the large data sets regularly generated by simulations or captured through experiments. This chapter seeks to answer key questions about frameworks for parallelizing visualization algorithms: What is the nature of these frameworks? How are they used? How do they parallelize processing? What problems result from parallelization? And how can optimizations be incorporated?
