Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms

Abstract

We present a general method for deploying parallel linear genetic programming (LGP) to the PC and Xbox 360 video game console by using a publicly available common framework for the devices called XNA (for ''XNA's Not Acronymed''). By constructing the LGP within this framework, we effectively produce an LGP ''game'' for PC and XBox 360 that displays results as they evolve. We use the GPU of each device to parallelize fitness evaluation and the mutation operator of the LGP algorithm, thus providing a general LGP implementation suitable for parallel computation on heterogeneous devices. While parallel GP implementations on PCs are now common, both the implementation of GP on a video game console using GPU and the construction of a GP around a framework for heterogeneous devices are novel contributions. The objective of this work is to describe how to implement the parallel execution of LGP in order to use the underlying hardware (especially GPU) on the different platforms while still maintaining loyalty to the general methodology of the LGP algorithm built for the common framework. We discuss the implementation of texture-based data structures and the sequential and parallel algorithms built for their use on both CPU and GPU. Following the description of the general algorithm, the particular tailoring of the implementations for each hardware platform is described. Sequential (CPU) and parallel (GPU-based) algorithm performance is compared on both PC and video game platforms using the metrics of GP operations per second, actual time elapsed, This work is based on an earlier work: Deployment

Faster GPU-based genetic programming using a two-dimensional stack

“…An average 60 fold speedup is reported over an interpreted version implemented in C (Nordin and Banzhaf, 1995). Alternative parallel processor platforms have been considered for the deployment of GP such as Microsoft's XBox 360 (Wilson and Banzhaf (2008) and Wilson and Banzhaf (2010)). Field Programable Gate Arrays (FPGAs) have also been harnessed by the GP community to accelerate GP.…”

Parallel Genetic Programming on Graphics Processing Units

“…Even the performance of GP on graphic devices of video game consoles was analyzed [36][37][38], but PC implementations of GP have demonstrated to be faster and more robust. However, it was with the current high level programming languages [4,34], namely NVIDIA's CUDA and OpenCL, that GP implementations using GP becomes popular, specially in much larger/real world applications.…”

Graphics processing units and genetic programming: an overview

“…While the Cell processor [74] and games consoles [75] are viable hardware platforms they also suffer the steep human programmer learning curve of GPUs. (Which we described in the previous section.)…”