ABSTRACT
Understanding vector fields resulting from large scientific simulations is an important and often difficult task. Integral curves (ICs)—curves that are tangential to a vector field at each point-are a powerful visualization method in this context. The application of an integral curve-based visualization to a very large vector field data represents a significant challenge, due to the non-local and data-dependent nature of IC computation. The application requires a careful balancing of computational demands placed on I/O, memory, communication, and processors. This chapter reviews several different parallelization approaches, based on established parallelization paradigms (across particles and data blocks) and current advanced techniques for achieving a scalable, parallel performance on very large data sets.
