Performance for stride 2

Hello,

I tried to leverage the speedups from `@turbo` for a case with stride-2 access of the input data . This actually seemed to degrade performance compared to `@inbounds`, and it got even worse with `@tturbo`. I realize that it is much more tricky with non-contiguous data, but wasn't expecting this much of a degradation. Didn't manage to find much info, so perhaps it is a rare use case. Is this to be expected or should something be done differently?

I tried to boil it down to the following mwe.

Thanks!

```
using LoopVectorization

function test_stride2_inbounds(out, x)
@inbounds for k = 3:length(x)÷2 # @turbo does not work, unsure why
acc = 0
acc += x[2k-1]
acc += x[2k-2]
acc += x[2k-3]
acc += x[2k-4]
acc += x[2k-5]
out[k] = acc
end

return out
end

function test_stride2_turbo(out, x)
@turbo for k = 3:length(x)÷2 # @turbo does not work, unsure why
acc = 0
acc += x[2k-1]
acc += x[2k-2]
acc += x[2k-3]
acc += x[2k-4]
acc += x[2k-5]
out[k] = acc
end

return out
end

function test_stride2_tturbo(out, x)
@tturbo for k = 3:length(x)÷2 # @turbo does not work, unsure why
acc = 0
acc += x[2k-1]
acc += x[2k-2]
acc += x[2k-3]
acc += x[2k-4]
acc += x[2k-5]
out[k] = acc
end

return out
end

function test_stride1_turbo(out, x)
@turbo for k = 3:length(x)÷2 # @turbo does not work, unsure why
acc = 0
acc += x[k-1]
acc += x[k-2]
acc += x[k-3]
acc += x[k-4]
acc += x[k-5]
out[k] = acc
end

return out
end

function test_stride1_tturbo(out, x)
@tturbo for k = 3:length(x)÷2 # @turbo does not work, unsure why
acc = 0
acc += x[k-1]
acc += x[k-2]
acc += x[k-3]
acc += x[k-4]
acc += x[k-5]
out[k] = acc
end

return out
end

x = rand(-1000:1000, 2^17)
out1, out2, out3, out4, out5 = (similar(x) for _=1:5)

println("# Threads = $(Threads.nthreads())")

println("\ntest_stride2_inbounds:")
display( @benchmark test_stride2_inbounds(out1, x) )
println("\ntest_stride2_turbo:")
display( @benchmark test_stride2_turbo(out2, x) )
println("\ntest_stride2_tturbo:")
display( @benchmark test_stride2_tturbo(out3, x) )
println("\ntest_stride1_turbo:")
display( @benchmark test_stride1_turbo(out4, x) )
println("\ntest_stride1_tturbo:")
display( @benchmark test_stride1_tturbo(out5, x) )
```

```
# Threads = 10

test_stride2_inbounds:
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 53.300 μs … 867.500 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 56.800 μs ┊ GC (median): 0.00%
Time (mean ± σ): 59.665 μs ± 23.947 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

█▃ ▁ █▄▁ ▁▁ ▃▇ ▄▇▂ ▂▁ ▄▁ ▁ ▂
██▇▅███▇▅███▆▇███▇▆███▅▇▆█████▅██▇█▆▆▅▅██▇▆▅▁█▆▅▄▆▅▄▆▄▄▁▄▄▁▅ █
53.3 μs Histogram: log(frequency) by time 74.6 μs <

Memory estimate: 0 bytes, allocs estimate: 0.

test_stride2_turbo:
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 80.900 μs … 3.360 ms ┊ GC (min … max): 0.00% … 0.00%
Time (median): 110.400 μs ┊ GC (median): 0.00%
Time (mean ± σ): 134.626 μs ± 133.504 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

█
▄██▄▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▂▂▂▂▂▂▂▁▁▁▂▂▂▂▁▂▁▂▂▁▂▂▂▂▂▂▂▁▂▂▂▂▁▂ ▂
80.9 μs Histogram: frequency by time 1.01 ms <

Memory estimate: 0 bytes, allocs estimate: 0.

test_stride2_tturbo:
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 181.400 μs … 10.707 ms ┊ GC (min … max): 0.00% … 0.00%
Time (median): 209.000 μs ┊ GC (median): 0.00%
Time (mean ± σ): 242.770 μs ± 203.921 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

█▅█▅▃▃▃▂▁▁▁▁ ▁
██████████████▇▇▇█▇▇▇▇▇▆▆▆▇▅▆▅▄▆▄▆▅▆▆▄▄▃▄▅▅▄▅▅▅▆▃▅▅▅▅▅▅▅▄▄▄▅▃ █
181 μs Histogram: log(frequency) by time 938 μs <

Memory estimate: 0 bytes, allocs estimate: 0.

test_stride1_turbo:
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 24.900 μs … 956.400 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 31.100 μs ┊ GC (median): 0.00%
Time (mean ± σ): 32.754 μs ± 30.490 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

▅▅▄▂▁ ▄█▆▇▇▆▃▂▁▆▄▂▄▃▁▁ ▂
██████▇▇▆▆████████████████▇█▇█▇▇▇▆▇▅▅▅▅▃▄▂▃▃▃▄▅▄▃▄▃▄▅▃▄▃▃▃▃▂ █
24.9 μs Histogram: log(frequency) by time 52 μs <

Memory estimate: 0 bytes, allocs estimate: 0.

test_stride1_tturbo:
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 12.100 μs … 2.709 ms ┊ GC (min … max): 0.00% … 0.00%
Time (median): 15.500 μs ┊ GC (median): 0.00%
Time (mean ± σ): 21.181 μs ± 51.142 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

▆█▆▃▄▁▁▂ ▂
███████████▇▇▆▇▇▆▆▆▅▅▅▅▅▅▅▅▆▅▅▃▄▅▄▄▅▃▅▅▅▄▄▃▁▄▅▅▅▅▅▅▅▅▅▁▃▄▄▅ █
12.1 μs Histogram: log(frequency) by time 132 μs <

Memory estimate: 0 bytes, allocs estimate: 0.
```

Beginne damit, das Problem mit den fünf Funktionen `test_stride1_*` und `test_stride2_*` in der bereitgestellten MWE zu reproduzieren, und vergleiche dabei `@inbounds`, `@turbo` und `@tturbo`. Untersuche die Einstiegspunkte der Schleifenvektorisierung für diese Makros und ermittle, ob die Verlangsamung bei stride-2 und die nicht unterstützte Schleife erwartungsgemäß sind. Als erledigt gilt die Aufgabe, wenn die Ursache dokumentiert und entschieden wurde, ob ein gezielter Fix oder eine explizite Einschränkung angemessen ist.

Vom Indexierungsmodell aus dem Issue-Text verfasst.