  Genetic Programming (GP) is a computationally intensive technique which also
has a high degree of natural parallelism. Parallel computing architectures have
become commonplace especially with regards Graphics Processing Units (GPU).
Hence, versions of GP have been implemented that utilise these highly parallel
computing platforms enabling significant gains in the computational speed of GP
to be achieved. However, recently a two dimensional stack approach to GP using
a multi-core CPU also demonstrated considerable performance gains. Indeed,
performances equivalent to or exceeding that achieved by a GPU were
demonstrated. This paper will demonstrate that a similar two dimensional stack
approach can also be applied to a GPU based approach to GP to better exploit
the underlying technology. Performance gains are achieved over a standard
single dimensional stack approach when utilising a GPU. Overall, a peak
computational speed of over 55 billion Genetic Programming Operations per
Second are observed, a two fold improvement over the best GPU based single
dimensional stack approach from the literature.
