相关实验视频
Updated: Jun 13, 2025

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Operation of the Collaborative Composite Manufacturing CCM System
Published on: October 1, 2019
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便携式加快CMS计算工作流程与协处理器作为服务
, A Hayrapetyan1, A Tumasyan1,2
1Yerevan Physics Institute, Yerevan, Armenia.
概括
在共处理器上优化网络推理服务 (SONIC) 加快了科学实验的机器学习推理. 这种方法通过将任务卸载给GPU等协处理器来提高数据处理吞吐量.
科学领域:
- 高能物理计算计算 高能物理计算
- 机器学习基础设施的基础设施.
背景情况:
- 像CERN LHC的CMS实验这样的科学实验面临着不断升级的计算需求.
- 单独的中央处理单元 (CPU) 可能无法满足未来的性能需求.
- 协处理器为特定的计算任务提供了架构优势.
研究的目的:
- 探索用于大规模数据处理的协处理器优化网络推理服务 (SONIC) 方法.
- 评估SONIC作为科学工作流程服务的部署.
- 为了证明将机器学习推断任务卸载到共处理器的好处.
主要方法:
- 实现了CMS实验中的数据处理工作流.
- 在CPU上执行主要的工作流程,并将机器学习推断任务卸载给图形处理单元 (GPU) 作为共处理器.
- 在谷歌云和普渡大学二级计算中心进行了实验.
主要成果:
- 在共处理器上实现了机器学习算法的个体加速.
- 在整个数据处理工作流程中显著提高了吞吐量.
- 验证了 SONIC 方法在各种云计算和本地计算环境中的有效性.
结论:
- 索尼克方法有效地加速了机器学习在科学数据处理中的推断.
- 索尼克能够在不同类型的共处理器中实现高共处理器利用率和工作流的可移植性.
- 这种方法可以推广到各种协处理器,并部署在本地CPU上,而不会降低性能.
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