论文标题
研究图形IPU在粒子物理中应用的潜力
Studying the potential of Graphcore IPUs for applications in Particle Physics
论文作者
论文摘要
本文介绍了GraphCore智能处理单元（IPU）在粒子物理应用的背景下进行的首次研究。 IPU是一种针对机器学习优化的新型处理器。对基于神经网络的事件模拟，多种散发校正以及在IPU，GPU和CPU上实现的风味标记进行了比较，并使用各种神经网络体系结构和超参数进行了比较。此外，在IPU和GPU上实现了用于轨道重建的Kálmán滤波器。结果表明，IPU在满足粒子物理中迅速增加的计算需求方面具有巨大的希望。
This paper presents the first study of Graphcore's Intelligence Processing Unit (IPU) in the context of particle physics applications. The IPU is a new type of processor optimised for machine learning. Comparisons are made for neural-network-based event simulation, multiple-scattering correction, and flavour tagging, implemented on IPUs, GPUs and CPUs, using a variety of neural network architectures and hyperparameters. Additionally, a Kálmán filter for track reconstruction is implemented on IPUs and GPUs. The results indicate that IPUs hold considerable promise in addressing the rapidly increasing compute needs in particle physics.
