GPU加速混合粒子场分子动力学:多节点/多GPU实现和OCCAM代码的大规模基准
Rosario Esposito1, Giuseppe Mensitieri1, You-Liang Zhou2
1Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Napoli, Italy.
Journal of computational chemistry
|May 14, 2025
概括
混合粒子场分子动力学 (hPF-MD) 的新并行化策略使大规模模拟成为可能. 这种以GPU为中心的方法显著减少了数据交换,使复杂的分子研究在更少的资源下可行.
科学领域:
- 计算物理 计算物理
- 分子动力学分子动力学
- 高性能计算 高性能计算
背景情况:
- 分子动力学模拟对于理解复杂系统至关重要.
- 将模拟规模扩大到数十亿个粒子,带来了重大的计算挑战.
- 现有的多CPU架构限制了分子动力学研究的规模和范围.
研究的目的:
- 为混合粒子场分子动力学 (hPF-MD) 模拟开发一个大规模并行策略.
- 为分布式多GPU架构优化OCCAM代码.
- 为了使数十亿颗粒子的系统能够进行hPF-MD模拟.
主要方法:
- 实现GPU居民代码,使CPU和GPU之间的数据交换最小化.
- 使用多节点多GPU架构来增强计算能力.
- 解决处理大型输入文件和内存占用方面的挑战.
主要成果:
- 与以前的多CPU版本相比,显示出显著的性能改进.
- 成功对高达100亿颗粒子的系统进行hPF-MD模拟的基准测试.
- 展示了使用中等计算资源进行大规模模拟的可行性.
结论:
- 拟议的hPF-MD并行化策略对于大规模分子模拟非常有效.
- 这种进步允许对以前无法获得的数十亿颗粒子系统进行系统研究.
- 开辟了分子洞察复杂科学问题的新途径.
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