Accelerating and Benchmarking Radix-k Image Compositing at Large Scale

Abstract

Radix-k was introduced in 2009 as a configurable image compositing algorithm. The ability to tune it by selecting k-values allows it to benefit more from pixel reduction and compression optimizations than its predecessors. This paper describes such optimizations in Radix-k, analyzes their effects, and demonstrates improved performance and scalability. In addition to bounding and run-length encoding pixels, k-value selection and load balance are regulated at run-time. Performance is systematically analyzed for an array of process counts, image sizes, and HPC and graphics clusters. Analyses are performed using compositing of synthetic images and also in the context of a complete volume renderer and scientific data. We demonstrate increased performance over binary swap and show that 64 megapixels can be composited at rates of 0.08 seconds, or 12.5 frames per second, at 32 K processes.