v0.3.0 To-do List about blis.jl HOT 2 OPEN

# Generic striding is good:
julia> @benchmark view(A, 1:2:1000, :)*B setup=(A=rand(Float64, 1000, 1000); B=rand(Float64, 1000, 1000))
BenchmarkTools.Trial: 22 samples with 1 evaluation.
 Range (min … max):  228.803 ms … 233.134 ms  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     229.311 ms               ┊ GC (median):    0.00%
 Time  (mean ± σ):   229.954 ms ±   1.277 ms  ┊ GC (mean ± σ):  0.00% ± 0.00%

julia> using BLIS; @benchmark view(A, 1:2:1000, :)*B setup=(A=rand(Float64, 1000, 1000); B=rand(Float64, 1000, 1000))
BenchmarkTools.Trial: 213 samples with 1 evaluation.
 Range (min … max):  20.919 ms …  21.676 ms  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     21.161 ms               ┊ GC (median):    0.00%
 Time  (mean ± σ):   21.137 ms ± 109.760 μs  ┊ GC (mean ± σ):  0.00% ± 0.00%

# Mixed precision not reaching BLIS directly:
julia> A=rand(Float64, 1000, 1000); B=rand(Float32, 1000, 1000);
julia> using Cthulhu; @descend A*B
...
   %7  = invoke copyto_axcheck!(::Matrix{Float64},::Matrix{Float32})::Matrix{Float64}
   %8  = invoke gemm!(::Char,::Char,::Float64,::Matrix{Float64},::Matrix{Float64},::Float64,::Matrix{Float64})::Matrix{Float64}
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