Markov Chain Monte Carlo simulations of lattice Quantum Chromodynamics
(QCD) are the only known tool to investigate non-perturbatively the theory
of the strong interaction and are required to perform precision tests of
the Standard Model of Particle Physics.
As the Markov Chain is a serial process, the sole option for improving the
sampling rate is accelerating each individual update step.
Heterogeneous clusters of GPU-accelerated nodes offer large total memory
bandwidth which can be used to speed-up our application, openQxD-1.1,
which is dominated by inversions of the Dirac operator, a large sparse
matrix.
In this work we investigate offloading the inversion to GPU using the
lattice-QCD library QUDA, and our early results demonstrate a significant
potential speed-up in the time-to-solution for state-of-the-art problem
sizes.
Minimal extensions to the existing QUDA library are required for our
specific physics programme while greatly enhancing the performance
portability of our code and retaining the reliability and robustness of
existing applications in openQxD-1.1.
Our new interface will enable us to utilize pre-exascale infrastructure and
reduce the systematic uncertainty in our physics predictions by
incorporating the effects of quantum electromagnetism (QED) in our simulations.