Computational biology has increasingly turned to agent-based modeling to explore complex biological systems. Biological diffusion (diffusion, decay, secretion, and uptake) is a key driver of biological tissues. GPU computing can vastly accelerate the diffusion and decay operators in the partial differential equations used to represent biological transport in an agent-based biological modeling system. In this paper, we utilize OpenACC to accelerate the diffusion portion of PhysiCell, a cross-platform agent-based biosimulation framework. We demonstrate an almost 40x speedup on the state-of-the-art NVIDIA A100 GPU compared to a serial run on AMD's EPYC 7742. We also demonstrate 9x speedup on the 64 core AMD EPYC 7742 multicore platform. By using OpenACC for both the CPUs and the GPUs, we maintain a single source code base, thus creating a portable yet performant solution. With the simulator's most significant computational bottleneck significantly reduced, we can continue cancer simulations over much longer times.
翻译:生物扩散(扩散、衰变、分解和吸收)是生物组织的关键驱动力。 GPU 计算可以大大加速用于代理生物模型系统中代表生物运输的局部差异方程式的传播和衰变操作者。在本文中,我们利用 OpenACC 加速PhysisiCell 的传播部分,这是一个基于跨平台的代理物生物模拟框架。我们展示了比AMD EPYC 7742 的序列运行快近40倍的NVIDIA A100 GPU。我们还展示了64 AMD EPYC 7742 多核心平台的9x加速。我们通过对 CPU 和 GPU 都使用 OpenACC 来保持单一源代码基础, 从而创建了便携式但有性能的解决方案。由于模拟器最显著的计算瓶壳, 我们可以持续更长时间的癌症模拟。