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MetaQCD.jl

Inspired by the LatticeQCD.jl package by Akio Tomiya et al.

For detailed information on how to use this package, see the docs.

Features

  • [x] Simulations of 4D-SU(3) Yang-Mills (Pure Gauge) theory
  • [x] Simulations of full lattice QCD with arbitrary number of flavours (Staggered, Wilson-Clover)
  • [x] Metadynamics
  • [x] PT-MetaD
  • [x] Several update algorithms (HMC, Metropolis, Heatbath, Overrelaxation)
  • [x] Several symplectic integrators for HMC (Leapfrog, OMF2, OMF4)
  • [x] Gradient flow with variable integrators (Euler, RK2, RK3, RK3W7)
  • [x] Improved Gauge actions (Symanzik tree, Iwasaki, DBW2)
  • [x] Improved Topological charge definitions (clover, rectangle clover-improved)
  • [x] Wilson(-Clover) fermions
  • [x] Staggered fermions
  • [x] RHMC to simulate odd number of flavours
  • [x] Even-odd preconditioner for Wilson(-Clover)
  • [x] Even-odd preconditioner for Staggered
  • [ ] Mass-splitting preconditioner / Hasenbusch trick
  • [x] Full support for GPU Backends (CUDA and ROCm for now)
  • [x] Multi-node parallelism using MPI

Installation

First make sure you have Julia version 1.9.4 or above installed. You can use juliaup for that or just install the release from the Julia website.

The particular version you want is ultimately determined by the GPU you want to use.

Newer GPUs need more recent versions of Julia for compatibility.

Then:

  1. Clone the latest release onto your machine.
  2. Open Julia in the directory which you cloned the repo into, with the project specific environment. This can either be done by starting Julia with the command line argument "–project" or by activating the environment within an opened Julia instance via the package manager:
using Pkg
Pkg.activate(".")

Or you can switch to package manager mode by typing "]" and then do

pkg> activate .
  1. Instantiate the project to install all the dependencies using the package manager:
Pkg.instantiate()

or

pkg> instantiate

If you want to use a GPU (not yet fully ready), make sure you not only have CUDA.jl or AMDGPU.jl installed, but also a fairly recent version of the CUDA Toolkit or ROCm.