GPU coprocessors as a service for deep learning inference in high energy physics
GPU coprocessors as a service for deep learning inference in high energy physics
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GPU协处理器作为高能物理中深度学习推理的服务
DOI：
10.1088/2632-2153/abec21
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
J. Krupa;Kelvin Lin;M. Acosta Flechas;Jack T. Dinsmore;Javier Mauricio Duarte;P. Harris;S. Hauck;B. Holzman;Shih-Chieh Hsu;T. Klijnsma;Miaoyuan Liu;K. Pedro;D. Rankin;Natchanon Suaysom;Matthew Trahms;N. Tran
中科院分区：
文献类型：
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作者：
J. Krupa;Kelvin Lin;M. Acosta Flechas;Jack T. Dinsmore;Javier Mauricio Duarte;P. Harris;S. Hauck;B. Holzman;Shih-Chieh Hsu;T. Klijnsma;Miaoyuan Liu;K. Pedro;D. Rankin;Natchanon Suaysom;Matthew Trahms;N. Tran
未来十年，大型科学实验对计算的需求预计将大幅增长。同一时间段内，CPU性能提升将受到限制。在欧洲核子研究组织大型强子对撞机（LHC）中，随着对撞机升级为高亮度运行，这两个问题将相互面对。图形处理单元 (GPU) 等替代处理器可以解决这种对抗，前提是算法可以充分加速。在许多情况下，通过采用深度学习算法可以最大程度地提高算法速度。我们对在高能物理数据重建工作流程中使用基于 GPU 的硬件加速进行深度学习推理进行了全面的探索。我们提出了几个现实的例子，并讨论了协处理器无缝集成的策略，以便大型强子对撞机在整个运行过程中能够保持（如果不是超过的话）当前的性能。
In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC), these two issues will confront one another as the collider is upgraded for high luminosity running. Alternative processors such as graphics processing units (GPUs) can resolve this confrontation provided that algorithms can be sufficiently accelerated. In many cases, algorithmic speedups are found to be largest through the adoption of deep learning algorithms. We present a comprehensive exploration of the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics. We present several realistic examples and discuss a strategy for the seamless integration of coprocessors so that the LHC can maintain, if not exceed, its current performance throughout its running.