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UID:submissions.pasc-conference.org_PASC23_sess176_pap120@linklings.com
SUMMARY:Graph Contractions for Calculating Correlation Functions in Lattic
 e QCD
DESCRIPTION:Paper\n\nJie Chen and Robert Edwards (Jefferson Lab) and Weizh
 en Mao (William & Mary)\n\nComputing correlation functions for many-partic
 le 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 because of th
 e complex nature of a hadron composed of quarks with many degrees of freed
 om. In particular, a correlation function can be calculated through a sum 
 of all possible pairs of quark contractions dictated by <em>Wick</em>'s th
 eorem. Because the number of terms of this sum can be very large for any h
 adronic system of interest, fast evaluation of the sum faces several chall
 enges: an extremely large number of contractions, a huge memory footprint,
  and the speed of contractions. In this paper, we present a Lattice QCD an
 alysis software suite, <em>Redstar</em>, 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,
  <em>Redstar</em> represents every term in the sum of a correlation functi
 on by a graph, applies efficient graph algorithms to reduce the number of 
 contractions, to lower the cost of the computations, and to minimize the t
 otal memory footprint. Moreover, <em>Redstar</em> carries out the contract
 ions on either CPUs or GPUs utilizing an internal and highly efficient <em
 >Hadron</em> contraction library. Specifically, we illustrate some importa
 nt algorithmic optimizations of <em>Redstar</em>, show key design features
  of <em>Hadron</em> library, and present the speedup values due to the opt
 imizations along with performance figures for calculating six correlations
  functions on four computing platforms.\n\nDomain: Physics\n\nSession Chai
 r: Sinéad Ryan (Trinity College, Hamilton Mathematics Institute)
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