论文标题
PDFFLOW：Parton分布在GPU上起作用
PDFFlow: parton distribution functions on GPU
论文作者
论文摘要
我们提出了PDFFLOW，这是一种用于快速评估Parton分配功能（PDFS）的新软件，该软件专为具有硬件加速器的平台而设计。 PDF对于通过蒙特卡洛模拟技术计算粒子物理物理学是必不可少的。在给定的动量分数和能量量表上对夸克和gluon的一套通用PDF的评估需要首次通过LHAPDF项目实施插值算法。 PDFFLOW使用Google的TensorFlow库扩展并实现了这些插值算法，从而提供了执行PDF评估的功能，从而充分利用了多线程CPU和GPU设置。我们在与粒子物理社区相关的多种情况下基准了该库的性能。
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.
