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在GPU上深度对流物理参数化方案的大规模加速度算法
Yongfei Wang1, Junping Wang2, Jiarui Tian3
1China Energy Dadu River Hydropower Development Co., Ltd., Chengdu, China.
PloS one
|January 8, 2025
概括
这项研究加速了使用GPU计算的大气模型的计算密集型张-麦克法兰 (ZM) 深度对流参数化方案. 新的基于GPU的算法显著加快了计算速度,提高了天气预报和气候模拟效率.
科学领域:
- 计算科学是一种计算科学.
- 大气科学 大气科学
- 气候建模 气候建模
背景情况:
- 准确的天气预报和气候模拟依赖于大气循环模型.
- 张-麦克法兰 (ZM) 深度对流参数化方案至关重要,但计算上昂贵,阻碍了模型的效率.
- 在ZM方案中,可并行计算提供了加速的潜力.
研究的目的:
- 为张-麦克法兰 (ZM) 深度对流参数化方案开发和评估基于GPU的加速算法.
- 与基于CPU的实现相比,评估性能改进.
- 提高大气循环模型的运行效率,用于气候研究和危险预测.
主要方法:
- 开发了一维和二维基于GPU的加速算法,用于使用CUDA C. CUDA C. 的ZM方案.
- 实现并将算法与单个CPU和OpenMP多核CPU版本进行比较.
- 利用CUDA流媒体技术进行性能优化,专注于CPU-GPU数据传输效率.
主要成果:
- 与CPU实现相比,在没有I/O传输的情况下实现了413.6×的加速度.
- 在存在I / O传输的情况下,在A100 GPU上展示了350.1×的显著加速度.
- 验证了拟议的GPU算法的显著加速效应和效率增长.
结论:
- 拟议的基于GPU的加速算法显著提高了ZM深度对流参数化方案的计算效率.
- 这一进步对于提高气候模型和深度对流参数化方案的速度和适用性至关重要.
- 优化的数据传输进一步提高了性能,使得GPU加速成为运营气候建模的实际解决方案.
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