在 ProtoDUNE 数据处理中使用 GPU 加快机器学习推理
Tejin Cai1, Kenneth Herner2, Tingjun Yang2
1Department of Physics and Astronomy, York University, 4700 Keele Street, Toronto, M3J 1P3 ON Canada.
概括
基于云的GPU服务器显著加速中微子数据处理. 一个100GPU服务器的数据处理速度是CPU的两倍,但网络带宽的限制需要仔细分配工作.
科学领域:
- 高能物理 高能物理
- 计算物理 计算物理
- 数据科学数据科学数据科学
背景情况:
- 中微子物理学实验产生了大量的数据集,需要高效的处理.
- 事件重建是一个计算密集的任务,对数据分析至关重要.
- 可扩展的计算资源对于处理当前和未来的实验需求至关重要.
研究的目的:
- 评估基于云计算的GPU加速推断服务器对中微子数据批次任务的性能.
- 将GPU加速算法的处理速度与基于CPU的方法进行比较.
- 确定大规模数据再处理中的潜在瓶和挑战.
主要方法:
- 利用了来自ProtoDUNE实验的探测器数据.
- 采用标准的DUNE网格工作提交工具用于数据再处理.
- 运行了数千个并发的网格工作,使用GPU和CPU版本的算法处理数据.
- 在GPU和CPU实现之间比较处理时间.
主要成果:
- 一个基于100GPU的云服务器有效地满足了处理需求.
- 事件处理算法的GPU版本是最新CPU上的CPU版本的两倍.
- 在GPU运行期间的高数据传输速率给网络带宽带来了挑战.
结论:
- 基于云的GPU加速为加速中微子数据重建提供了可行的解决方案.
- 必须考虑网络基础设施,以避免在大型GPU数据处理过程中出现瓶.
- 未来的实验需要进一步优化和分布式计算策略.
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