Efficient open-source GPU implementation for multi-agent autochemotactic 2D modeling

We present a high-performance open-source GPU implementation for simulating large ensembles of autochemotactic active Brownian particles in two dimensions. The code couples continuous overdamped dynamics of self-propelled particle with a discrete chemoattractant field. This software enables simulations of autochemotactic channel formation, collective search strategies, multi-target foraging dynamics, and first-passage problems in both free exploration and goal-directed navigation scenarios, where effects of rotational and translational chemotactic responses, chemosecretion, resetting and target reinforcement are important. Our implementation addresses these challenges through GPU-accelerated algorithms: two-dimensional parallelization of chemoattractant deposition, multiplicative renormalization for efficient field decay, spatial hashing for particle interactions, and asynchronous multi-stream execution for ensemble statistics. We benchmark on NVIDIA A100 hardware and characterize algorithmic scaling with number of particles and chemoattractant deposition cutoff radius. We also compare GPU execution times with these on multi-core Intel Xeon and AMD EPYC CPU systems. We release the implementation as open-source software with examples and documentation, addressing community calls for unified computational tools in motile active matter.本研究提出一种高性能开源图形处理器（Graphics Processing Unit, GPU）实现方案，用于二维环境下大量自趋化活性布朗粒子系综的模拟。该代码将自推进粒子的连续过阻尼动力学与离散趋化因子场进行耦合。本软件可支持多种模拟任务，包括自趋化通道形成、集体搜索策略、多目标觅食动力学，以及自由探索与目标导向导航场景下的首通问题，其中旋转与平移趋化响应、化学分泌、重置及目标强化等效应均为关键影响因素。本实现方案通过GPU加速算法攻克上述技术挑战：涵盖趋化因子沉积的二维并行化、用于高效场衰减的乘法重整化、用于粒子相互作用的空间哈希技术，以及用于系综统计的异步多流执行机制。我们在NVIDIA A100硬件平台上开展基准测试，并针对粒子数量与趋化因子沉积截止半径两个维度，对算法的缩放性能进行了表征分析。此外，我们还将GPU的运行时长与多核英特尔至强（Intel Xeon）及AMD EPYC CPU系统的运行时长进行了对比。我们将该实现方案以开源软件的形式发布，并附带示例代码与文档，以回应活性运动物质领域社区对统一计算工具的迫切需求。