标题： 在基于树的遗传编程中实现种群级并行性以全面加速GPU 显示英文标题

标题： Enabling Population-Level Parallelism in Tree-Based Genetic Programming for Comprehensive GPU Acceleration

摘要： 基于树的遗传编程（TGP）是一种广泛用于符号回归、分类和机器人控制等任务的进化算法。 由于运行TGP的计算需求非常大，GPU加速对于实现可扩展性能至关重要。 然而，高效的基于GPU的TGP执行仍然具有挑战性，主要是由于三个核心问题： （1）程序个体的结构异质性，（2）整合多级并行性的复杂性，以及（3）高性能CUDA执行与灵活的Python环境之间的不兼容性。 为了解决这些问题，我们提出了EvoGP，这是一个高性能框架，通过种群级并行执行来全面加速TGP。 首先，EvoGP引入了一种张量化表示，将可变大小的树编码为固定形状、内存对齐的数组，从而实现统一的内存访问和跨不同个体的并行计算。 其次， EvoGP采用了一种自适应并行策略，根据数据集大小动态结合个体内部和个体间的并行性，确保在广泛的任务范围内实现高GPU利用率。 第三，EvoGP将自定义的CUDA内核嵌入到PyTorch运行时中，实现了与Python环境如Gym、MuJoCo、Brax和Genesis的无缝集成。 全面的实验表明，EvoGP在最先进的基于GPU的TGP实现上实现了高达140倍的速度提升，同时保持了竞争性的准确性，并在大规模种群下显著提高了可扩展性。 EvoGP是开源的，可在以下链接获取：https://github.com/EMI-Group/evogp。 显示英文摘要

摘要： Tree-based Genetic Programming (TGP) is a widely used evolutionary algorithm for tasks such as symbolic regression, classification, and robotic control. Due to the intensive computational demands of running TGP, GPU acceleration is crucial for achieving scalable performance. However, efficient GPU-based execution of TGP still remains challenging, primarily due to three core issues: (1) the structural heterogeneity of program individuals, (2) the complexity of integrating multiple levels of parallelism, and (3) the incompatibility between high-performance CUDA execution and flexible Python-based environments. To address these issues, we propose EvoGP, a high-performance framework tailored for comprehensive GPU acceleration of TGP via population-level parallel execution. First, EvoGP introduces a tensorized representation that encodes variable-sized trees into fixed-shape, memory-aligned arrays, enabling uniform memory access and parallel computation across diverse individuals. Second, EvoGP adopts an adaptive parallelism strategy that dynamically combines intra- and inter-individual parallelism based on dataset size, ensuring high GPU utilization across a broad spectrum of tasks. Third, EvoGP embeds custom CUDA kernels into the PyTorch runtime, achieving seamless integration with Python-based environments such as Gym, MuJoCo, Brax, and Genesis. Comprehensive experiments show that EvoGP achieves up to 140x speedup over state-of-the-art GPU-based TGP implementations, while maintaining competitive accuracy and significantly improving scalability under large population sizes. EvoGP is open source and accessible at: https://github.com/EMI-Group/evogp.