Switch from avx to turbo

brenhinkeller · Jun 20, 2021 · b61f718 · b61f718 · brenhinkeller · Jun 20, 2021
1 parent 7322144
commit b61f718
Show file tree

Hide file tree

Showing 6 changed files with 25 additions and 25 deletions.
diff --git a/src/ArrayStats/ArrayStats.jl b/src/ArrayStats/ArrayStats.jl
@@ -25,7 +25,7 @@
     Fill a Boolean mask of dimensions `size(A)` that is false wherever `A` is `NaN`
     """
     function nanmask!(mask, A)
-        @avx for i=1:length(A)
+        @turbo for i=1:length(A)
             mask[i] = A[i]==A[i]
         end
         return mask
@@ -42,7 +42,7 @@
     Replace all `NaN`s in A with zeros of the same type
     """
     function zeronan!(A::Array)
-        @avx for i ∈ eachindex(A)
+        @turbo for i ∈ eachindex(A)
             Aᵢ = A[i]
             A[i] = ifelse(Aᵢ==Aᵢ, Aᵢ, 0)
         end
@@ -287,7 +287,7 @@
     function _nanmean(A::Array{<:Integer}, W, ::Colon)
         n = zero(eltype(W))
         m = zero(promote_type(eltype(W), eltype(A)))
-        @avx for i ∈ eachindex(A)
+        @turbo for i ∈ eachindex(A)
             Wᵢ = W[i]
             n += Wᵢ
             m += Wᵢ * A[i]
@@ -300,7 +300,7 @@
         T2 = promote_type(eltype(W), eltype(A))
         n = zero(T1)
         m = zero(T2)
-        @avx for i ∈ eachindex(A)
+        @turbo for i ∈ eachindex(A)
             Aᵢ = A[i]
             Wᵢ = W[i]
             t = Aᵢ==Aᵢ
@@ -333,12 +333,12 @@
         w = sum(W.*mask, dims=region)
         s = sum(A.*W.*mask, dims=region) ./ w
         d = A .- s # Subtract mean, using broadcasting
-        @avx for i ∈ eachindex(d)
+        @turbo for i ∈ eachindex(d)
             dᵢ = d[i]
             d[i] = (dᵢ * dᵢ * W[i]) * mask[i]
         end
         s .= sum(d, dims=region)
-        @avx for i ∈ eachindex(s)
+        @turbo for i ∈ eachindex(s)
             s[i] = sqrt((s[i] * n[i]) / (w[i] * (n[i] - 1)))
         end
         return s
@@ -370,7 +370,7 @@
         Tm = promote_type(eltype(W), eltype(A))
         w = zero(Tw)
         m = zero(Tm)
-        @avx for i ∈ eachindex(A)
+        @turbo for i ∈ eachindex(A)
             Aᵢ = A[i]
             Wᵢ = W[i]
             t = Aᵢ==Aᵢ
@@ -381,7 +381,7 @@
         mu = m / w
         Tmu = typeof(mu)
         s = zero(Tmu)
-        @avx for i ∈ eachindex(A)
+        @turbo for i ∈ eachindex(A)
             Aᵢ = A[i]
             d = Aᵢ - mu
             s += ifelse(Aᵢ==Aᵢ, d * d * W[i], zero(Tmu))

diff --git a/src/ArrayStats/nancov.jl b/src/ArrayStats/nancov.jl
@@ -2,7 +2,7 @@ function _nancov(x::AbstractVector, y::AbstractVector, corrected::Bool, μᵪ::N
     # Calculate covariance
     σᵪᵧ = ∅ = zero(promote_type(typeof(μᵪ), typeof(μᵧ), Int))
     n = 0
-    @avx for i ∈ indices((x,y))
+    @turbo for i ∈ indices((x,y))
             δᵪ = x[i] - μᵪ
             δᵧ = y[i] - μᵧ
             δ² = δᵪ * δᵧ

diff --git a/src/ArrayStats/nanmean.jl b/src/ArrayStats/nanmean.jl
@@ -54,7 +54,7 @@ function _nanmean(A, ::Colon)
     Tₒ = Base.promote_op(/, eltype(A), Int)
     n = 0
     Σ = ∅ = zero(Tₒ)
-    @avx for i ∈ eachindex(A)
+    @turbo for i ∈ eachindex(A)
         Aᵢ = A[i]
         notnan = Aᵢ==Aᵢ
         n += notnan
@@ -65,7 +65,7 @@ end
 function _nanmean(A::AbstractArray{<:Integer}, ::Colon)
     Tₒ = Base.promote_op(/, eltype(A), Int)
     Σ = zero(Tₒ)
-    @avx for i ∈ eachindex(A)
+    @turbo for i ∈ eachindex(A)
         Σ += A[i]
     end
     return Σ / length(A)
@@ -130,7 +130,7 @@ function staticdim_nanmean_quote(static_dims::Vector{Int}, N::Int)
   quote
     ∅ = zero(eltype(B))
     Bᵥ = $Bᵥ
-    @avx $loops
+    @turbo $loops
     return B
   end
 end

diff --git a/src/ArrayStats/nanstd.jl b/src/ArrayStats/nanstd.jl
@@ -37,7 +37,7 @@ export nanstd
 
 sqrt!(x::Number) = sqrt(x)
 function sqrt!(A::AbstractArray)
-    @avx for i ∈ eachindex(A)
+    @turbo for i ∈ eachindex(A)
         A[i] = sqrt(A[i])
     end
     return A

diff --git a/src/ArrayStats/nansum.jl b/src/ArrayStats/nansum.jl
@@ -53,7 +53,7 @@ end
 function _nansum(A, ::Colon)
     Tₒ = Base.promote_op(+, eltype(A), Int)
     Σ = ∅ = zero(Tₒ)
-    @avx for i ∈ eachindex(A)
+    @turbo for i ∈ eachindex(A)
         Aᵢ = A[i]
         notnan = Aᵢ==Aᵢ
         Σ += ifelse(notnan, A[i], ∅)
@@ -63,7 +63,7 @@ end
 function _nansum(A::AbstractArray{<:Integer}, ::Colon)
     Tₒ = Base.promote_op(+, eltype(A), Int)
     Σ = zero(Tₒ)
-    @avx for i ∈ eachindex(A)
+    @turbo for i ∈ eachindex(A)
         Σ += A[i]
     end
     return Σ
@@ -126,7 +126,7 @@ function staticdim_nansum_quote(static_dims::Vector{Int}, N::Int)
   quote
     ∅ = zero(eltype(B))
     Bᵥ = $Bᵥ
-    @avx $loops
+    @turbo $loops
     return B
   end
 end

diff --git a/src/ArrayStats/nanvar.jl b/src/ArrayStats/nanvar.jl
@@ -49,15 +49,15 @@ function _nanvar(::Nothing, corrected::Bool, A, ::Colon)
     Tₒ = Base.promote_op(/, eltype(A), Int)
     n = 0
     Σ = ∅ = zero(Tₒ)
-    @avx for i ∈ eachindex(A)
+    @turbo for i ∈ eachindex(A)
         Aᵢ = A[i]
         notnan = Aᵢ==Aᵢ
         n += notnan
         Σ += ifelse(notnan, Aᵢ, ∅)
     end
     μ = Σ / n
     σ² = ∅ = zero(typeof(μ))
-    @avx for i ∈ eachindex(A)
+    @turbo for i ∈ eachindex(A)
         δ = A[i] - μ
         notnan = δ==δ
         σ² += ifelse(notnan, δ * δ, ∅)
@@ -69,12 +69,12 @@ function _nanvar(::Nothing, corrected::Bool, A::AbstractArray{<:Integer}, ::Colo
     Tₒ = Base.promote_op(/, eltype(A), Int)
     n = length(A)
     Σ = zero(Tₒ)
-    @avx for i ∈ eachindex(A)
+    @turbo for i ∈ eachindex(A)
         Σ += A[i]
     end
     μ = Σ / n
     σ² = zero(typeof(μ))
-    @avx for i ∈ eachindex(A)
+    @turbo for i ∈ eachindex(A)
         δ = A[i] - μ
         σ² += δ * δ
     end
@@ -89,7 +89,7 @@ function _nanvar(μ::Number, corrected::Bool, A, ::Colon)
     # Reduce all the dims!
     n = 0
     σ² = ∅ = zero(typeof(μ))
-    @avx for i ∈ eachindex(A)
+    @turbo for i ∈ eachindex(A)
         δ = A[i] - μ
         notnan = δ==δ
         n += notnan
@@ -101,7 +101,7 @@ function _nanvar(μ::Number, corrected::Bool, A::AbstractArray{<:Integer}, ::Col
     # Reduce all the dims!
     σ² = zero(typeof(μ))
     if μ==μ
-        @avx for i ∈ eachindex(A)
+        @turbo for i ∈ eachindex(A)
             δ = A[i] - μ
             σ² += δ * δ
         end
@@ -118,12 +118,12 @@ end
 #     N = sum(mask, dims=region)
 #     Σ = sum(A.*mask, dims=region)./N
 #     δ = A .- Σ # Subtract mean, using broadcasting
-#     @avx for i ∈ eachindex(δ)
+#     @turbo for i ∈ eachindex(δ)
 #         δᵢ = δ[i]
 #         δ[i] = ifelse(mask[i], δᵢ * δᵢ, 0)
 #     end
 #     B .= sum(δ, dims=region)
-#     @avx for i ∈ eachindex(B)
+#     @turbo for i ∈ eachindex(B)
 #         B[i] = B[i] / max(N[i] - corrected, 0)
 #     end
 #     return B
@@ -190,7 +190,7 @@ function staticdim_nanvar_quote(static_dims::Vector{Int}, N::Int)
   quote
     ∅ = zero(eltype(B))
     Bᵥ = $Bᵥ
-    @avx $loops
+    @turbo $loops
     return B
   end
 end