# Optimization: How to make sure XOR is performed in chunks

**URL:** <https://discourse.julialang.org/t/optimization-how-to-make-sure-xor-is-performed-in-chunks/33947>\
**Category:** New to Julia\
**Created:** [January 29, 2020, 8:19pm UTC](https://discourse.julialang.org/t/optimization-how-to-make-sure-xor-is-performed-in-chunks/33947 "2020-01-29T20:19:17Z")\
**Posts on this page:** 1\
**Showing post:** 32

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**Author:** ![lungben](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/lungben/32/12314_2.png) [@lungben](https://discourse.julialang.org/u/lungben)\
**Post date:** [November 30, 2020, 5:10pm UTC](https://discourse.julialang.org/t/optimization-how-to-make-sure-xor-is-performed-in-chunks/33947/32 "2020-11-30T17:10:05Z")

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A bit late to the party, but I just had a similar use case today.  
The fastest method I could find was working on UInt8 instead of BitArrays, `count_ones` and LoopVectorization:

```julia
function hamming_distance(h1, h2)
    s = 0
    @avx for i = 1:length(h1)
        s += count_ones(xor(h1[i], h2[i]))
    end
    s
end

h1 = Vector{UInt8}(randstring(hash_length))
h2 = Vector{UInt8}(randstring(hash_length))

julia> @btime hamming_distance($h1, $h2)
  11.813 ns (0 allocations: 0 bytes)

```

The method above (based on the same BitVectors) is slower for me:

```julia
function make_bitvector(v::Vector{UInt8})
           siz = sizeof(v)
           bv = falses(siz<<3)
           unsafe_copyto!(reinterpret(Ptr{UInt8}, pointer(bv.chunks)), pointer(v), siz)
           bv
end

h1b = make_bitvector(h1)
h2b = make_bitvector(h2)
buf = similar(h1b)

julia> @btime hamming($h1b, $h2b, $buf)
  16.933 ns (0 allocations: 0 bytes)

```

Is bitwise Hamming Distance on GPU an option to get even more speed? With my very limited GPU knowledge I could not find a way to make it faster (or even the same speed) as on CPU.

Edit: btw. I was able to beat the performance of a ~150 LOC Cython implementation with less than 30 lines of Julia code 😉

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