Use custom static reduction for CPU

JuliaStream.jl/src/ThreadedStream.jl

@@ -63,12 +63,46 @@ function nstream!(data::VectorData{T}, _) where {T}
   end
 end
 
+# Threads.@threads/Threads.@spawn doesn't support OpenMP's firstprivate, etc
+function static_par_ranged(f::Function, range::Int, n::Int)
+  stride = range ÷ n
+  rem = range % n
+  strides = map(0:n) do i
+    width = stride + (i < rem ? 1 : 0)
+    offset = i < rem ? (stride + 1) * i : ((stride + 1) * rem) + (stride * (i - rem))
+    (offset, width)
+  end
+  ccall(:jl_enter_threaded_region, Cvoid, ())
+  try
+    foreach(wait, map(1:n) do group
+      (offset, size) = strides[group]
+      task = Task(() -> f(group, offset+1, offset+size))
+      task.sticky = true
+      ccall(:jl_set_task_tid, Cvoid, (Any, Cint), task, group-1) # ccall, so 0-based for group
+      schedule(task)
+    end)
+  finally
+    ccall(:jl_exit_threaded_region, Cvoid, ())
+  end
+end
+
 function dot(data::VectorData{T}, _) where {T}
-  partial = zeros(T, Threads.nthreads())
-  Threads.@threads for i = 1:data.size
-    @inbounds partial[Threads.threadid()] += data.a[i] * data.b[i]
+  partial = Vector{T}(undef,  Threads.nthreads())
+  static_par_ranged(data.size, Threads.nthreads()) do group, startidx, endidx
+    acc = zero(T)
+    @fastmath for i = startidx:endidx
+      @inbounds acc += data.a[i] * data.b[i]
+    end
+    @inbounds partial[group] = acc
   end
   return sum(partial)
+  # This doesn't do well on aarch64 because of the excessive Threads.threadid() ccall 
+  # and inhibited vectorisation from the lack of @fastmath 
+  # partial = zeros(T, Threads.nthreads())
+  # Threads.@threads for i = 1:data.size
+  #   @inbounds partial[Threads.threadid()] += (data.a[i] * data.b[i])
+  # end
+  # return sum(partial)
 end
 
 function read_data(data::VectorData{T}, _)::VectorData{T} where {T}