ABSTRACT
Image compositing is a fundamental part of high performance visualization on large-scale parallel machines. Aside from reading a data set from storage, compositing is the most expensive part of the parallel rendering pipeline because it requires communication among a large number of processes. On a modern supercomputer, compositing may generate literally hundreds of thousands of messages. Thus, developing compositing algorithms that scale with growing machine size is crucial. Such algorithms have enabled, for example, wall-size images that are tens of megapixels in resolution to be composited at interactive frame rates from all of the nodes of some of the world’s largest supercomputers and visualization clusters. First, this chapter discusses a history of the classic parallel image compositing algorithms: direct-send and binary-swap. From there, the discussion moves to optimizations that have been proposed over the years, from scheduling to compression and load balancing. Advanced compositing on modern supercomputing architectures, however, is the main
and radix-k for HPC machines.
