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.

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 性能的提升将受到限制。在欧洲核子研究中心大型强子对撞机 (LHC) 上，随着对撞机升级为高光度运行，这两个问题将相互对抗。只要算法能够得到足够的加速，诸如图形处理单元 (GPU) 之类的替代处理器就可以解决这种冲突。在许多情况下，通过采用深度学习算法，算法加速被发现是最大的。我们全面探索了在高能物理的数据重建工作流中使用基于 GPU 的硬件加速进行深度学习推理。