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use SIMD.jl for x86 and naive_findmin for :aarch64 #151

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use SIMD.jl for x86 and naive_findmin for :aarch64 #151

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353f023
using naive_findmin implementation
m-fila May 14, 2025
6a6a6c8
slice dij
m-fila May 14, 2025
30e237f
diff implementation on :aarch64
Moelf May 14, 2025
c9fe359
cleanup(?) code
Moelf May 14, 2025
dd97a88
fix off by one
Moelf May 14, 2025
8df5c2d
format
Moelf May 15, 2025
18be031
Merge branch 'main' into naive_findmin
giordano May 19, 2025
c3a5e08
Merge branch 'main' into naive_findmin
Moelf May 21, 2025
70cad13
Merge branch 'main' into naive_findmin
Moelf May 21, 2025
36704d9
Merge branch 'main' into naive_findmin
Moelf Jun 18, 2025
0cb6ff5
Merge branch 'main' into naive_findmin
Moelf Jun 27, 2025
0fc6ccf
Merge branch 'main' into naive_findmin
Moelf Jul 20, 2025
bfbae69
Merge branch 'main' into naive_findmin
Moelf Aug 27, 2025
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2 changes: 0 additions & 2 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,6 @@ CodecZlib = "944b1d66-785c-5afd-91f1-9de20f533193"
CodecZstd = "6b39b394-51ab-5f42-8807-6242bab2b4c2"
EnumX = "4e289a0a-7415-4d19-859d-a7e5c4648b56"
Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
LoopVectorization = "bdcacae8-1622-11e9-2a5c-532679323890"
LorentzVectorHEP = "f612022c-142a-473f-8cfd-a09cf3793c6c"
LorentzVectors = "3f54b04b-17fc-5cd4-9758-90c048d965e3"
MuladdMacro = "46d2c3a1-f734-5fdb-9937-b9b9aeba4221"
Expand All @@ -32,7 +31,6 @@ EDM4hep = "0.4.0"
EnumX = "1.0.4"
JSON = "0.21"
Logging = "1.9"
LoopVectorization = "0.12.170"
LorentzVectorHEP = "0.1.6"
LorentzVectors = "0.4.3"
Makie = "0.20, 0.21, 0.22"
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2 changes: 0 additions & 2 deletions src/PlainAlgo.jl
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@@ -1,5 +1,3 @@
using LoopVectorization

"""
dist(i, j, rapidity_array, phi_array)

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1 change: 0 additions & 1 deletion src/TiledAlgoLL.jl
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Expand Up @@ -5,7 +5,6 @@

using Logging
using Accessors
using LoopVectorization

# Include struct definitions and basic operations
include("TiledAlgoLLStructs.jl")
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12 changes: 3 additions & 9 deletions src/Utils.jl
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Expand Up @@ -147,13 +147,7 @@ array. The use of `@turbo` macro gives a significant performance boost.
- `best`: The index of the minimum value in the `dij` array.
"""
fast_findmin(dij, n) = begin
# findmin(@inbounds @view dij[1:n])
best = 1
@inbounds dij_min = dij[1]
@turbo for here in 2:n
newmin = dij[here] < dij_min
best = newmin ? here : best
dij_min = newmin ? dij[here] : dij_min
end
dij_min, best
x = @fastmath foldl(min, dij)
i = findfirst(==(x), dij)::Int
x, i
end
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