PDFFlow: Parton distribution functions on GPU

We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators. PDFs are essential for the calculation of particle physics observables through Monte Carlo simulation techniques. The evaluation of a generic set of PDFs for quarks and gluon at a given momentum fraction and energy scale requires the implementation of interpolation algorithms as introduced for the first time by the LHAPDF project. PDFFlow extends and implements these interpolation algorithms using Google's TensorFlow library providing the capabilities to perform PDF evaluations taking fully advantage of multi-threading CPU and GPU setups. We benchmark the performance of this library on multiple scenarios relevant for the particle physics community.我们提出PDFFlow：一款面向硬件加速器平台、用于快速求值部分子分布函数（Parton Distribution Functions, PDFs）的新型软件。在通过蒙特卡洛模拟技术计算粒子物理可观测量的场景中，PDFs是不可或缺的关键工具。针对夸克与胶子的通用PDF集合，在给定动量份额与能标下的求值过程，需实现LHAPDF项目首次提出的插值算法。PDFFlow基于谷歌TensorFlow库实现并扩展了上述插值算法，可充分利用多线程CPU与GPU部署架构的算力优势，完成PDF求值任务。我们针对粒子物理领域相关的多种典型场景，对该库的性能进行了基准测试。