PDFFlow: parton distribution functions on GPU
This work addresses the need for faster PDF computations in particle physics simulations, though it is incremental as it builds on existing interpolation algorithms from LHAPDF.
The authors tackled the problem of slow parton distribution function (PDF) evaluations in particle physics by developing PDFFlow, a software that uses TensorFlow for GPU acceleration, achieving significant speed improvements in benchmarks relevant to the community.
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.