Faster GPU Based Genetic Programming Using A Two Dimensional Stack

TL;DR

This paper introduces a two-dimensional stack approach for GPU-based Genetic Programming, significantly improving computational speed by over two times compared to traditional methods, leveraging GPU architecture effectively.

Contribution

It adapts a two-dimensional stack method to GPU-based GP, achieving substantial performance gains over existing single-dimensional stack approaches.

Findings

Peak speed of over 55 billion GP operations per second

Twofold speed improvement over previous GPU single-dimensional stack methods

Demonstrates effective exploitation of GPU architecture for GP

Abstract

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.