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49 lines
1.5 KiB
Julia
49 lines
1.5 KiB
Julia
###
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### "THE BEER-WARE LICENSE":
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### Alberto Ramos wrote this file. As long as you retain this
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### notice you can do whatever you want with this stuff. If we meet some
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### day, and you think this stuff is worth it, you can buy me a beer in
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### return. <alberto.ramos@cern.ch>
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###
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### file: test_adapt.jl
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### created: Mon Jun 6 12:01:36 2022
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###
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using LatticeGPU, Test, CUDA
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T = Float64
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lp = SpaceParm{4}((16,16,16,16), (4,4,4,4), BC_PERIODIC, (0,0,0,0,0,0))
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gp = GaugeParm{T}(SU3{T}, 6.1, 1.0)
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ymws = YMworkspace(SU3, T, lp)
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randomize!(ymws.mom, lp, ymws)
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U = exp.(ymws.mom)
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Ucp = deepcopy(U)
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# First Integrate very precisely up to t=2 (Wilson)
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println(" # Very precise integration ")
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wflw = wfl_rk3(Float64, 0.0004, 1.0E-7)
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flw(U, wflw, 5000, gp, lp, ymws)
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pl_exact = Eoft_plaq(U, gp, lp, ymws)
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cl_exact = Eoft_clover(U, gp, lp, ymws)
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println(" - Plaq: ", pl_exact)
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println(" - Clover: ", cl_exact)
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Ufin = deepcopy(U)
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# Now use Adaptive step size integrator:
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for tol in (1.0E-4, 1.0E-5, 1.0E-6, 1.0E-7, 1.0E-8)
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local wflw = wfl_rk3(Float64, 0.0001, tol)
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U .= Ucp
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ns, eps = flw_adapt(U, wflw, 2.0, gp, lp, ymws)
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pl = Eoft_plaq(U, gp, lp, ymws)
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cl = Eoft_clover(U, gp, lp, ymws)
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println(" # Adaptive integrator (tol=$tol): ", ns, " steps")
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U .= U ./ Ufin
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maxd = CUDA.mapreduce(dev_one, max, U, init=0.0)
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println(" - Plaq: ", pl," [diff: ", abs(pl-pl_exact), "; ",
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maxd, "]")
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println(" - Clover: ", cl, " [diff: ", abs(cl-cl_exact), "; ",
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maxd, "]")
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end
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