GPU协处理器作为高能物理中深度学习推理的服务
GPU coprocessors as a service for deep learning inference in high energy physics
摘要：
在接下来的十年中，大型科学实验中对计算的需求预计将大幅增长。在同一时间段内，CPU性能的提高将受到限制。在欧洲核子研究中心的大型强子对撞机（LHC）中，这两个问题将在对撞机升级为高亮度运行时相互面对。其他处理器，如图形处理器（GPU），可以解决这个问题，只要算法可以得到充分的加速。在许多情况下，通过采用深度学习算法，发现算法加速最大。我们全面探索了在高能物理的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。我们提出了几个现实的例子，并讨论了一个战略的无缝集成的协处理器，使大型强子对撞机可以保持，如果不超过，其当前的性能在其整个运行。
英文摘要：
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.
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