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https://igit.ific.uv.es/alramos/latticegpu.jl.git
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Alberto Ramos
parent
c378648508
commit
76d0b66b4b
9 changed files with 515 additions and 322 deletions
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@ -9,154 +9,198 @@
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### created: Thu Jul 15 15:16:47 2021
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###
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un(t) = t <: Union{Group, Complex}
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function field(::Type{T}, lp::SpaceParm) where {T <: Union{Group, Algebra}}
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sz = lp.iL..., lp.ndim
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sz = lp.bsz, lp.ndim, lp.rsz
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if (T == SU2)
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As = StructArray{SU2}((ones(ComplexF64, sz), zeros(ComplexF64, sz)))
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# As = StructArray{SU2}(undef, sz, unwrap=un)
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return CuArray{SU2, 3}(undef, sz)
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elseif (T == SU2alg)
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As = StructArray{SU2alg}((zeros(Float64, sz),
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zeros(Float64, sz),
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zeros(Float64, sz)))
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# As = StructArray{SU2alg}(undef, sz, unwrap=un)
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return CuArray{SU2alg, 3}(undef, sz)
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elseif (T == SU3)
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As = StructArray{SU3}((ones(ComplexF64, sz), zeros(ComplexF64, sz), zeros(ComplexF64, sz), zeros(ComplexF64, sz), ones(ComplexF64, sz), zeros(ComplexF64, sz)))
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# As = StructArray{SU3}(undef, sz, unwrap=un)
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return CuArray{SU3, 3}(undef, sz)
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# As = Array{SU3, lp.ndim+1}(undef, sz)
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# CUDA.@sync begin
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# CUDA.@cuda threads=kp.threads blocks=kp.blocks krnl_SU3_zero!(As, lp)
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# end
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elseif (T == SU3alg)
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As = StructArray{SU3alg}((zeros(Float64, sz),
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zeros(Float64, sz),
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zeros(Float64, sz),
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zeros(Float64, sz),
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zeros(Float64, sz),
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zeros(Float64, sz),
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zeros(Float64, sz),
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zeros(Float64, sz)))
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# As = StructArray{SU3alg}(undef, sz, unwrap=un)
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return CuArray{SU3alg, 3}(undef, sz)
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# As = Array{SU3alg, lp.ndim+1}(undef, sz)
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# CUDA.@sync begin
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# CUDA.@cuda threads=kp.threads blocks=kp.blocks krnl_SU3alg_zero!(As, lp)
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# end
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end
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return replace_storage(CuArray, As)
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end
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function randomn!(X)
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function krnl_SU3_one!(G, lp::SpaceParm)
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if (eltype(X) == SU2alg)
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randn!(CURAND.default_rng(), LazyRows(X).t1)
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randn!(CURAND.default_rng(), LazyRows(X).t2)
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randn!(CURAND.default_rng(), LazyRows(X).t3)
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elseif (eltype(X) == SU3alg)
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randn!(CURAND.default_rng(), LazyRows(X).t1)
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randn!(CURAND.default_rng(), LazyRows(X).t2)
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randn!(CURAND.default_rng(), LazyRows(X).t3)
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randn!(CURAND.default_rng(), LazyRows(X).t4)
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randn!(CURAND.default_rng(), LazyRows(X).t5)
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randn!(CURAND.default_rng(), LazyRows(X).t6)
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randn!(CURAND.default_rng(), LazyRows(X).t7)
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randn!(CURAND.default_rng(), LazyRows(X).t8)
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end
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return nothing
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end
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function zero!(X)
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if (eltype(X) == SU2alg)
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fill!(LazyRows(X).t1, 0.0)
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fill!(LazyRows(X).t2, 0.0)
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fill!(LazyRows(X).t3, 0.0)
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end
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if (eltype(X) == SU3alg)
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fill!(LazyRows(X).t1, 0.0)
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fill!(LazyRows(X).t2, 0.0)
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fill!(LazyRows(X).t3, 0.0)
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fill!(LazyRows(X).t4, 0.0)
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fill!(LazyRows(X).t5, 0.0)
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fill!(LazyRows(X).t6, 0.0)
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fill!(LazyRows(X).t7, 0.0)
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fill!(LazyRows(X).t8, 0.0)
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# CUDA.@sync begin
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# CUDA.@cuda threads=kp.threads blocks=kp.blocks krnl_SU3alg_zero!(X, lp)
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# end
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end
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if (eltype(X) == SU2)
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fill!(LazyRows(X).t1, complex(1.0))
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fill!(LazyRows(X).t2, complex(0.0))
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end
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if (eltype(X) == SU3)
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fill!(LazyRows(X).u11, complex(1.0))
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fill!(LazyRows(X).u12, complex(0.0))
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fill!(LazyRows(X).u13, complex(0.0))
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fill!(LazyRows(X).u21, complex(0.0))
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fill!(LazyRows(X).u22, complex(1.0))
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fill!(LazyRows(X).u23, complex(0.0))
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# CUDA.@sync begin
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# CUDA.@cuda threads=kp.threads blocks=kp.blocks krnl_SU3_zero!(X, lp)
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# end
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end
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return nothing
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end
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function norm2(X)
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d = 0.0
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if (eltype(X) == SU2alg)
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d = CUDA.mapreduce(x->x^2, +, LazyRows(X).t1) +
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CUDA.mapreduce(x->x^2, +, LazyRows(X).t2) +
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CUDA.mapreduce(x->x^2, +, LazyRows(X).t3)
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elseif (eltype(X) == SU3alg)
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d = CUDA.mapreduce(x->x^2, +, LazyRows(X).t1) +
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CUDA.mapreduce(x->x^2, +, LazyRows(X).t2) +
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CUDA.mapreduce(x->x^2, +, LazyRows(X).t3) +
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CUDA.mapreduce(x->x^2, +, LazyRows(X).t4) +
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CUDA.mapreduce(x->x^2, +, LazyRows(X).t5) +
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CUDA.mapreduce(x->x^2, +, LazyRows(X).t6) +
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CUDA.mapreduce(x->x^2, +, LazyRows(X).t7) +
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CUDA.mapreduce(x->x^2, +, LazyRows(X).t8)
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# d = CUDA.mapreduce(norm2, +, X)
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end
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return d
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end
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function krnl_SU3_zero!(G, lp::SpaceParm)
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X = map2latt((CUDA.threadIdx().x,CUDA.threadIdx().y,CUDA.threadIdx().z),
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(CUDA.blockIdx().x,CUDA.blockIdx().y,CUDA.blockIdx().z))
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b, r = CUDA.threadIdx().x, CUDA.blockIdx().x
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for id in 1:lp.ndim
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G[X,id].u11 = complex(1.0)
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G[X,id].u12 = complex(0.0)
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G[X,id].u13 = complex(0.0)
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G[X,id].u21 = complex(0.0)
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G[X,id].u22 = complex(1.0)
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G[X,id].u23 = complex(0.0)
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G[b,id,r] = SU3(1.0,0.0,0.0,0.0,1.0,0.0)
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end
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return nothing
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end
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function krnl_SU2_one!(G, lp::SpaceParm)
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b, r = CUDA.threadIdx().x, CUDA.blockIdx().x
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for id in 1:lp.ndim
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G[b,id,r] = SU2(1.0,0.0)
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end
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return nothing
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end
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function krnl_SU3alg_zero!(G, lp::SpaceParm)
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X = map2latt((CUDA.threadIdx().x,CUDA.threadIdx().y,CUDA.threadIdx().z),
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(CUDA.blockIdx().x,CUDA.blockIdx().y,CUDA.blockIdx().z))
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b, r = CUDA.threadIdx().x, CUDA.blockIdx().x
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for id in 1:lp.ndim
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G[X,id].t1 = 0.0
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G[X,id].t2 = 0.0
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G[X,id].t3 = 0.0
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G[X,id].t4 = 0.0
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G[X,id].t5 = 0.0
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G[X,id].t6 = 0.0
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G[X,id].t7 = 0.0
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G[X,id].t8 = 0.0
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G[b,id,r] = SU3alg(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)
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end
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return nothing
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end
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function krnl_SU2alg_zero!(G, lp::SpaceParm)
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b, r = CUDA.threadIdx().x, CUDA.blockIdx().x
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for id in 1:lp.ndim
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G[b,id,r] = SU2alg(0.0,0.0,0.0)
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end
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return nothing
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end
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function krnl_SU3alg_assign!(G, M, lp::SpaceParm)
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b, r = CUDA.threadIdx().x, CUDA.blockIdx().x
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for id in 1:lp.ndim
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G[b,id,r] = SU3alg(M[b,id,r,1], M[b,id,r,2], M[b,id,r,3], M[b,id,r,4],
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M[b,id,r,5], M[b,id,r,6], M[b,id,r,7], M[b,id,r,8])
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end
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return nothing
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end
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function krnl_SU2alg_assign!(G, M, lp::SpaceParm)
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b, r = CUDA.threadIdx().x, CUDA.blockIdx().x
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for id in 1:lp.ndim
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G[b,id,r] = SU2alg(M[b,id,r,1], M[b,id,r,2], M[b,id,r,3])
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end
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return nothing
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end
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function randomn!(X, lp)
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if (eltype(X) == SU2alg)
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# randn!(CURAND.default_rng(), LazyRows(X).t1)
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# randn!(CURAND.default_rng(), LazyRows(X).t2)
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# randn!(CURAND.default_rng(), LazyRows(X).t3)
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M = CuArray{Float64}(undef, lp.bsz, lp.ndim, lp.rsz, 3)
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randn!(CURAND.default_rng(), M)
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CUDA.@sync begin
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CUDA.@cuda threads=lp.bsz blocks=lp.rsz krnl_SU2alg_assign!(X, M, lp)
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end
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elseif (eltype(X) == SU3alg)
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# randn!(CURAND.default_rng(), LazyRows(X).t1)
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# randn!(CURAND.default_rng(), LazyRows(X).t2)
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# randn!(CURAND.default_rng(), LazyRows(X).t3)
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# randn!(CURAND.default_rng(), LazyRows(X).t4)
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# randn!(CURAND.default_rng(), LazyRows(X).t5)
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# randn!(CURAND.default_rng(), LazyRows(X).t6)
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# randn!(CURAND.default_rng(), LazyRows(X).t7)
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# randn!(CURAND.default_rng(), LazyRows(X).t8)
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M = CuArray{Float64}(undef, lp.bsz, lp.ndim, lp.rsz, 8)
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randn!(CURAND.default_rng(), M)
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CUDA.@sync begin
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CUDA.@cuda threads=lp.bsz blocks=lp.rsz krnl_SU3alg_assign!(X, M, lp)
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end
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end
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return nothing
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end
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function zero!(X, lp)
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if (eltype(X) == SU2alg)
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# fill!(LazyRows(X).t1, 0.0)
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# fill!(LazyRows(X).t2, 0.0)
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# fill!(LazyRows(X).t3, 0.0)
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CUDA.@sync begin
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CUDA.@cuda threads=lp.bsz blocks=lp.rsz krnl_SU2alg_zero!(X, lp)
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end
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end
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if (eltype(X) == SU3alg)
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# fill!(LazyRows(X).t1, 0.0)
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# fill!(LazyRows(X).t2, 0.0)
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# fill!(LazyRows(X).t3, 0.0)
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# fill!(LazyRows(X).t4, 0.0)
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# fill!(LazyRows(X).t5, 0.0)
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# fill!(LazyRows(X).t6, 0.0)
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# fill!(LazyRows(X).t7, 0.0)
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# fill!(LazyRows(X).t8, 0.0)
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CUDA.@sync begin
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CUDA.@cuda threads=lp.bsz blocks=lp.rsz krnl_SU3alg_zero!(X, lp)
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end
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end
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if (eltype(X) == SU2)
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# fill!(LazyRows(X).t1.re, 1.0)
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# fill!(LazyRows(X).t1.im, 0.0)
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# fill!(LazyRows(X).t2.re, 0.0)
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# fill!(LazyRows(X).t2.im, 0.0)
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CUDA.@sync begin
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CUDA.@cuda threads=lp.bsz blocks=lp.rsz krnl_SU2_one!(X, lp)
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end
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end
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if (eltype(X) == SU3)
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# fill!(LazyRows(X).u11.re, 1.0)
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# fill!(LazyRows(X).u11.im, 0.0)
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# fill!(LazyRows(X).u12.re, 0.0)
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# fill!(LazyRows(X).u12.im, 0.0)
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# fill!(LazyRows(X).u13.re, 0.0)
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# fill!(LazyRows(X).u13.im, 0.0)
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# fill!(LazyRows(X).u21.re, 0.0)
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# fill!(LazyRows(X).u21.im, 0.0)
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# fill!(LazyRows(X).u22.re, 1.0)
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# fill!(LazyRows(X).u22.im, 0.0)
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# fill!(LazyRows(X).u23.re, 0.0)
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# fill!(LazyRows(X).u23.im, 0.0)
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CUDA.@sync begin
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CUDA.@cuda threads=lp.bsz blocks=lp.rsz krnl_SU3_one!(X, lp)
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end
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end
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return nothing
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end
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function norm_field(X)
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return CUDA.mapreduce(norm2, +, X)
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# d = 0.0
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if (eltype(X) == SU2alg)
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# d = CUDA.mapreduce(x->x^2, +, LazyRows(X).t1) +
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# CUDA.mapreduce(x->x^2, +, LazyRows(X).t2) +
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# CUDA.mapreduce(x->x^2, +, LazyRows(X).t3)
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elseif (eltype(X) == SU3alg)
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# d = CUDA.mapreduce(x->x^2, +, LazyRows(X).t1) +
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# CUDA.mapreduce(x->x^2, +, LazyRows(X).t2) +
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# CUDA.mapreduce(x->x^2, +, LazyRows(X).t3) +
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# CUDA.mapreduce(x->x^2, +, LazyRows(X).t4) +
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# CUDA.mapreduce(x->x^2, +, LazyRows(X).t5) +
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# CUDA.mapreduce(x->x^2, +, LazyRows(X).t6) +
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# CUDA.mapreduce(x->x^2, +, LazyRows(X).t7) +
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# CUDA.mapreduce(x->x^2, +, LazyRows(X).t8)
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d = CUDA.mapreduce(norm2, +, X)
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end
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return d
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end
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