Abstract
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.
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