Comments (3)
chriselrod
commented on July 16, 2024
1
I fixed it without needing to create temporaries via, in VectorizationBase:
@inline stridedpointer(x::AbstractRange) = x
@inline load(r::AbstractRange, i::Tuple{<:Integer}) = @inbounds r[i[1] + 1]and in SIMDPirates:
@inline vload(r::AbstractRange{T}, i::Tuple{_MM{W}}) where {W,T} = vmuladd(svrange(Val{W}(), T), step(r), @inbounds r[i[1].i + 1])
@inline vload(r::UnitRange{T}, i::Tuple{_MM{W}}) where {W,T} = vadd(svrange(Val{W}(), T), @inbounds r[i[1].i + 1])
@inline vload(r::AbstractRange{T}, i::Tuple{_MM{W}}, ::Unsigned) where {W,T} = vmuladd(svrange(Val{W}(), T), step(r), @inbounds r[i[1].i + 1])
@inline vload(r::UnitRange{T}, i::Tuple{_MM{W}}, ::Unsigned) where {W,T} = vadd(svrange(Val{W}(), T), @inbounds r[i[1].i + 1])I haven't commited these changes yet, but will within the next few hours.
This is much slower than not having @avx, since it prevents LLVM's O(1) optimization of the loop.
Would be neat if we could avoid preventing that optimization.
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MasonProtter
commented on July 16, 2024
I have a use-case in mind that wouldn't benefit from O(1) optimization so I don't mind.
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chriselrod
commented on July 16, 2024
I'm closing this because I've now tagged a release where this works.
julia> f([1,2,3])
6.0
julia> f(1:3)
6.0Let me know if you have any more problems.
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Related Issues (20)
- How to cite? HOT 1
- @turbo can't find index when looping over sparse arrays HOT 3
- Error with accumulation in for loop HOT 4
- Performance issue with Zygote.jl-generated function within `@turbo` HOT 2
- Possible big TTFX regression HOT 1
- Julia 1.9 error: expected Static.StaticInt{1}, got a value of type Static.StaticInt{0} HOT 5
- Julia 1.9 error: @turbo for empty iterator HOT 3
- Trouble understanding @turbo and passing kwargs HOT 4
- `vtrunc(::Float64)` issue HOT 3
- Strange compile behavior for @turbo HOT 2
- is it possible to set @turbo thread = true/false at runtime? HOT 3
- LoopVectorization fail to compile on julia 32bit REPL
- AssertionError: M == 1 HOT 9
- Inconsistent results w/ and w/o @turbo HOT 6
- vfilter with multiple conditions HOT 2
- Memory corruption HOT 2
- Incorrect results using @turbo with linear array indexing HOT 1
- Weird/inconsistent behavior with constant lhs indexing inside @turbo loop HOT 2
- Suboptimal Choice of the Vecotrization Level for Image Convolution HOT 1
- Performance for stride 2
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