Typical computing systems based on general purpose processors (GPPs) can be extended with coarse-grained reconfigurable arrays (CGRAs) to provide higher performance and/or energy savings. In order for applications to take advantage of these computing systems, possibly including CGRAs varying in size, efficient dynamic compilation/mapping techniques are required. Dynamic mapping will be responsible for automatically moving computations originally running in the GPP to the CGRA. This paper presents our approach to dynamically map computations to CGRAs coupled to a GPP. Specifically, we evaluate the potential of the MegaBlock to accelerate the execution of a number of representative benchmarks when targeting an architecture based on a GPP and a CGRA. In addition, we show the impact on performance when using constant folding and propagation optimizations.
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