  Message-driven executions with over-decomposition of tasks constitute an
important model for parallel programming and have been demonstrated for
irregular applications. Supporting efficient execution of such message-driven
irregular applications on GPU systems presents a number of challenges related
to irregular data accesses and computations. In this work, we have developed
strategies including coalescing irregular data accesses and combining with data
reuse, and adaptive methods for hybrid executions to minimize idling. We have
integrated these runtime strategies into our {\em G-Charm} framework for
efficient execution of message-driven parallel applications on hybrid GPU
systems. We demonstrate our strategies for irregular applications with an
N-Body simulations and a molecular dynamics application and show that our
dynamic strategies result in 8-38\% reduction in execution times for these
irregular applications over the corresponding static strategies that are
amenable for regular applications.
