Parallel Algorithms for Image Histogramming and Connected Components with an Experimental Study

This paper presents efficient and portable implementations of two useful primitives in image processing algorithms, histogramming and
connected components. Our general framework is a single-address
space, distributed memory programming model. We use efficient
techniques for distributing and coalescing data as well as efficient
combinations of task and data parallelism. Our connected components
algorithm uses a novel approach for parallel merging which performs
drastically limited updating during iterative steps, and concludes
with a total consistency update at the final step. The algorithms have
been coded in Split-C and run on a variety of platforms. Our
experimental results are consistent with the theoretical analysis and
provide the best known execution times for these two primitives, even
when compared with machine specific implementations. More efficient
implementations of Split-C will likely result in even faster
execution times.
(Also cross-referenced as UMIACS-TR-94-133.)