  When simulating a lattice system near its critical temperature, local
algorithms for modeling the system's evolution can introduce very large
autocorrelation times into sampled data. This critical slowing down places
restrictions on the analysis that can be completed in a timely manner of the
behavior of systems around the critical point. Because it is often desirable to
study such systems around this point, a new algorithm must be introduced.
Therefore, we turn to cluster algorithms, such as the Swendsen-Wang algorithm
and the Wolff clustering algorithm. They incorporate global updates which
generate new lattice configurations with little correlation to previous states,
even near the critical point. We look to accelerate the rate at which these
algorithm are capable of running by implementing and benchmarking a parallel
implementation of each algorithm designed to run on GPUs under NVIDIA's CUDA
framework. A 17 and 90 fold increase in the computational rate was respectively
experienced when measured against the equivalent algorithm implemented in
serial code.
