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Graph Contractions for Calculating Correlation Functions in Lattice QCD

Conference ·
 [1];  [2];  [3]
  1. Scientific Computing, Jefferson Lab, Newport News, Virginia, United States of America
  2. Theory Center, Jefferson Lab, Newport News, Virginia, United States of America
  3. Department of Computer Science, William & Mary, Williamsburg, Virginia, United States
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Computing correlation functions for many-particle systems in Lattice QCD is vital to extract nuclear physics observables like the energy spectrum of hadrons such as protons. However, this type of calculation has long been considered to be very challenging and computing-resource intensive because of the complex nature of a hadron composed of quarks with many degrees of freedom. In particular, a correlation function can be calculated through a sum of all possible pairs of quark contractions, each of which is a batched tensor contraction, dictated by Wick's theorem. Because the number of terms of this sum can be very large for any hadronic system of interest, fast evaluation of the sum faces several challenges: an extremely large number of contractions, a huge memory footprint at runtime, and the speed of tensor contractions. In this paper, we present a Lattice QCD analysis software suite, Redstar, which addresses these challenges by utilizing novel algorithmic and software engineering methods targeting modern computing platforms such as many-core CPUs and GPUs. In particular, Redstar represents every term in the sum of a correlation function by a graph, applies efficient graph algorithms to reduce the number of contractions to lower the cost of computations, and minimizes the total memory footprint. Moreover, Redstar carries out the contractions on either CPUs or GPUs utilizing an internal and highly efficient Hadron contraction library. Specifically, we illustrate some important algorithmic optimizations of Redstar, show various key design features of Hadron library, and present the speedup values due to the optimizations along with performance figures for calculating six correlations functions on four computing platforms.

Research Organization:
Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Nuclear Physics (NP)
DOE Contract Number:
AC05-06OR23177
OSTI ID:
2203366
Report Number(s):
JLAB-CST-23-3931; DOE/OR/23177-7247
Resource Relation:
Conference: Proceedings of The Platform for Advanced Scientific Computing (PASC) Conference, Davos, Switzerland, June 26, 2023
Country of Publication:
United States
Language:
English

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An Efficient Graph Isomorphism Algorithm Based on Canonical Labeling and Its Parallel Implementation on GPU
  • No authors listed
  • 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing https://doi.org/10.1109/HPCC.and.EUC.2013.154
November 2013

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