replace Polyester with Floops

Project.toml

@@ -6,7 +6,7 @@ version = "0.8.4"
 [deps]
 IterativeSolvers = "42fd0dbc-a981-5370-80f2-aaf504508153"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-Polyester = "f517fe37-dbe3-4b94-8317-1923a5111588"
+FLoops = "cc61a311-1640-44b5-9fba-1b764f453329"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
@@ -15,6 +15,7 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 VectorizationBase = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f"
 
 [compat]
+FLoops = "0.2"
 IterativeSolvers = "0.9"
 ProgressMeter = "1.2"
 SparsityOperators = "0.3, 0.4"

src/RegularizedLeastSquares.jl

@@ -9,7 +9,7 @@ using IterativeSolvers
 using Random
 using VectorizationBase
 using VectorizationBase: shufflevector, zstridedpointer
-using Polyester
+using FLoops
 #@reexport using SparsityOperators
 using SparsityOperators: normalOperator, opEye
 using ProgressMeter

src/proximalMaps/ProxLLR.jl

@@ -46,7 +46,7 @@ function proxLLR!(
     try
         BLAS.set_num_threads(1)
         xᴸᴸᴿ = [Array{T}(undef, prod(blockSize), K) for _ = 1:Threads.nthreads()]
-        @batch for i ∈ CartesianIndices(StepRange.(TI(0), blockSize, shape .- 1))
+        @floop for i ∈ CartesianIndices(StepRange.(TI(0), blockSize, shape .- 1))
             @views xᴸᴸᴿ[Threads.threadid()] .= reshape(xp[i.+block_idx, :], :, K)
             # threshold singular values
             SVDec = svd!(xᴸᴸᴿ[Threads.threadid()])
@@ -183,7 +183,7 @@ function proxLLROverlapping!(
             shift_idx = (Tuple(is)..., 0)
             xs = circshift(xp, shift_idx)
 
-            @batch for i ∈ CartesianIndices(StepRange.(TI(0), blockSize, shape .- 1))
+            @floop for i ∈ CartesianIndices(StepRange.(TI(0), blockSize, shape .- 1))
                 @views xᴸᴸᴿ[Threads.threadid()] .= reshape(xs[i.+block_idx, :], :, K)
 
                 # threshold singular values