Beagle is a new software framework that enables execution of Genetic Programming tasks on the GPU. Currently available for symbolic regression, it processes individuals of the population and fitness cases for training in a way that maximizes throughput on extant GPU platforms. In this contribution, we report on the benchmarking of Beagle on the Feynman Symbolic Regression dataset and compare its performance with a fast CPU system called StackGP and the widely available PySR system under the same wall clock budget. We also report on the use of two different fitness functions, one a point-to-point error function, the other a correlation fitness function. The results demonstrate that the Beagle's GPU-aided Symbolic Regression significantly outperforms leading CPU-based frameworks.

翻译：Beagle是一种新的软件框架，能够在GPU上执行遗传规划任务。目前该框架适用于符号回归，其通过处理种群个体与训练适应度案例的方式，最大化现有GPU平台的吞吐量。本文报告了Beagle在费曼符号回归数据集上的基准测试结果，并在相同计算时间预算下，将其性能与基于CPU的快速系统StackGP以及广泛应用的PySR系统进行对比。我们还探讨了两种不同适应度函数的应用：一种采用逐点误差函数，另一种采用相关性适应度函数。实验结果表明，Beagle的GPU辅助符号回归显著优于基于CPU的主流框架。