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English(EN) Scalable Multi-GPU Simulation of 3D Multicellular Growth with RNN-Based Workload Balancing

新的基于RNN的框架加速了3D多细胞生长多GPU模拟

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个新颖的多GPU框架，旨在提高3D多细胞生长模拟的可扩展性。该框架集成了GPU加速、空间分箱和域分解，但其关键创新是一个基于RNN的负载均衡控制器。该控制器能够学习预测和纠正细胞移动和生长动态改变计算分布时出现的工作负载不平衡。与静态分区方法相比，该系统展示了显著的改进，将全局不平衡率从11.3%降低到3.5%，并将端到端运行时间降低了9.0%。 AI

影响 该框架通过实现更复杂、更高效的组织发育模拟，有望加速生物学研究。

排序理由 该集群包含一篇详细介绍新计算框架和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的基于RNN的框架加速了3D多细胞生长多GPU模拟

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该集群包含一篇详细介绍新计算框架和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Matvey Moisseyev, Huijing Du, Dandan Zheng, Chi Zhang, Hongfeng Yu ·

    基于RNN的负载均衡实现可扩展的多GPU三维多细胞生长模拟

    arXiv:2608.25890v1 Announce Type: cross Abstract: Detailed multicellular growth simulations based on subcellular element models (SEMs) can capture complex tissue development, but their element-level interactions impose substantial computational cost. This work presents a scalable…

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