  In this paper, we address the problem of efficient execution of a computation
pattern, referred to here as the irregular wavefront propagation pattern
(IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in
several image processing operations. In the IWPP, data elements in the
wavefront propagate waves to their neighboring elements on a grid if a
propagation condition is satisfied. Elements receiving the propagated waves
become part of the wavefront. This pattern results in irregular data accesses
and computations. We develop and evaluate strategies for efficient computation
and propagation of wavefronts using a multi-level queue structure. This queue
structure improves the utilization of fast memories in a GPU and reduces
synchronization overheads. We also develop a tile-based parallelization
strategy to support execution on multiple CPUs and GPUs. We evaluate our
approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs
and 2 multicore CPUs) using the IWPP implementations of two widely used image
processing operations: morphological reconstruction and euclidean distance
transform. Our results show significant performance improvements on GPUs. The
use of multiple CPUs and GPUs cooperatively attains speedups of 50x and 85x
with respect to single core CPU executions for morphological reconstruction and
euclidean distance transform, respectively.
