We address the problem of partitioning and dynamic load balancing on
clusters with heterogeneous hardware resources. We propose DRUM, a
model that encapsulates hardware resources and their interconnection
topology. DRUM provides monitoring facilities for dynamic evaluation
of communication, memory, and processing capabilities. Heterogeneity
is quantified by merging the information from the monitors to produce a
scalar number called "power." This power allows
DRUM to be used easily by existing load-balancing
procedures such as those in the Zoltan Toolkit while placing
minimal burden on application programmers. We demonstrate the use of
DRUM to guide load balancing in the adaptive solution of a Laplace
equation on a heterogeneous cluster. We observed a significant reduction
in execution time compared to traditional methods.
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