GSGP-CUDA -- a CUDA framework for Geometric Semantic Genetic Programming

TL;DR

GSGP-CUDA introduces a GPU-accelerated framework for Geometric Semantic Genetic Programming, achieving over 1,000 times faster performance than previous CPU-based implementations by leveraging intrinsic parallelism.

Contribution

This paper presents the first CUDA-based implementation of GSGP, significantly enhancing its computational efficiency through GPU parallelism.

Findings

Achieved over 1,000X speedup compared to sequential GSGP implementations.

Demonstrated effective utilization of GPU parallelism for GSGP.

Established GSGP-CUDA as the most efficient GSGP implementation to date.

Abstract

Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently then operating at the syntax level like most GP systems. Efficient implementations of GSGP in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP and the most efficient, which exploits the intrinsic parallelism of GSGP using GPUs. Results show speedups greater than 1,000X relative to the state-of-the-art sequential implementation.