TL;DR
PDFFlow is a GPU-accelerated software library that efficiently evaluates parton distribution functions using TensorFlow, significantly speeding up computations essential for particle physics simulations.
Contribution
It introduces a GPU-compatible implementation of PDF interpolation algorithms, leveraging TensorFlow for faster evaluations on multi-core CPUs and GPUs.
Findings
Achieves significant speedup in PDF evaluations on GPU and CPU.
Supports flexible and scalable PDF computations for particle physics.
Demonstrates performance benchmarks relevant to the physics community.
Abstract
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.
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