Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes of such experiments are rapidly increasing. The demand to process billions of neutrino events with many machine learning algorithm inferences creates a computing challenge. We explore a computing model in which heterogeneous computing with GPU coprocessors is made available as a web service. The coprocessors can be efficiently and elastically deployed to provide the right amount of computing for a given processing task. With our approach, Services for Optimized Network Inference on Coprocessors (SONIC), we integrate GPU acceleration specifically for the ProtoDUNE-SP reconstruction chain without disrupting the native computing workflow. With our integrated framework, we accelerate the most time-consuming task, track and particle shower hit identification, by a factor of 17. This results in a factor of 2.7 reduction in the total processing time when compared with CPU-only production. For this particular task, only 1 GPU is required for every 68 CPU threads, providing a cost-effective solution.
翻译:在以加速器为基础的中微子实验中,机器学习算法日益流行和运行在事件重建过程中日益盛行和发挥作用。这些复杂的算法可以计算成本。同时,这种实验的数据量正在迅速增加。要求用许多机器学习算法推论处理数十亿个中微子事件,这产生了计算挑战。我们探索一种计算模型,在这种模型中,与 GPU 共处理器的混合计算作为网络服务提供。可以高效率和有弹性地部署共处理器,为某一处理任务提供正确的计算量。在我们的方法中,为对共处理器的优化网络推断服务(SONIC),我们专门为ProtoDUNE-SP的重建链整合GPU加速,而不干扰本地的计算工作流程。我们结合了我们的综合框架,加快了最耗时的工作、跟踪和粒子冲洗器的识别,以17为系数。这导致总处理时间比CUU-只生产减少2.7倍。对于这一特定任务,每68个CPU线只需要1个GPU,提供具有成本效益的解决方案。