Extend LLVM 18 workaround to other float types.

[maleadt]

Extends #2937, fixes #2946

[giordano]

Can you do something like

@device_override @inline Base.FastMath.max_fast(x::$T, y::$T) where {T<:Union{Float16, Float32, Float64}} = ifelse(y > x, y, x)

just to avoid the loop
?

[maleadt]

I'm always wary for doing so, because the Base method may then end up being more specific (and we really want these to apply). In this case, Base doesn't use metaprogramming so I guess it could work..

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 89.31%. Comparing base (da38676) to head (97f7c1e).

Additional details and impacted files

@@           Coverage Diff           @@
##           master    #3016   +/-   ##
=======================================
  Coverage   89.31%   89.31%           
=======================================
  Files         148      148           
  Lines       12995    12995           
=======================================
  Hits        11606    11606           
  Misses       1389     1389           

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[github-actions]

CUDA.jl Benchmarks

Details

Benchmark suite	Current: 97f7c1e	Previous: da38676	Ratio
latency/precompile	55584314156 ns	55180480419 ns	1.01
latency/ttfp	7717855921.5 ns	7807018821.5 ns	0.99
latency/import	4025438307 ns	4140006046.5 ns	0.97
integration/volumerhs	9623132 ns	9623640.5 ns	1.00
integration/byval/slices=1	146664 ns	147119 ns	1.00
integration/byval/slices=3	425554 ns	426080.5 ns	1.00
integration/byval/reference	144978 ns	145207 ns	1.00
integration/byval/slices=2	286179 ns	286491 ns	1.00
integration/cudadevrt	103398 ns	103866 ns	1.00
kernel/indexing	14096 ns	14460 ns	0.97
kernel/indexing_checked	14790 ns	15250 ns	0.97
kernel/occupancy	679.2064516129033 ns	680.6470588235294 ns	1.00
kernel/launch	2083.2 ns	2197.4444444444443 ns	0.95
kernel/rand	15013 ns	18784 ns	0.80
array/reverse/1d	19771 ns	20177 ns	0.98
array/reverse/2dL_inplace	66721 ns	67023 ns	1.00
array/reverse/1dL	69914 ns	70421 ns	0.99
array/reverse/2d	21925 ns	22600 ns	0.97
array/reverse/1d_inplace	11577 ns	10092 ns	1.15
array/reverse/2d_inplace	13285 ns	13669 ns	0.97
array/reverse/2dL	74024 ns	74807 ns	0.99
array/reverse/1dL_inplace	66907 ns	67197 ns	1.00
array/copy	20556 ns	20909 ns	0.98
array/iteration/findall/int	157600.5 ns	159075 ns	0.99
array/iteration/findall/bool	139621 ns	140579 ns	0.99
array/iteration/findfirst/int	160826 ns	161050 ns	1.00
array/iteration/findfirst/bool	161498 ns	162329.5 ns	0.99
array/iteration/scalar	73528 ns	74588 ns	0.99
array/iteration/logical	213816 ns	216756.5 ns	0.99
array/iteration/findmin/1d	91481 ns	96345.5 ns	0.95
array/iteration/findmin/2d	121664 ns	122694 ns	0.99
array/reductions/reduce/Int64/1d	42940 ns	43621 ns	0.98
array/reductions/reduce/Int64/dims=1	50581 ns	44622.5 ns	1.13
array/reductions/reduce/Int64/dims=2	61611 ns	61757 ns	1.00
array/reductions/reduce/Int64/dims=1L	89000 ns	89064 ns	1.00
array/reductions/reduce/Int64/dims=2L	88050 ns	88156 ns	1.00
array/reductions/reduce/Float32/1d	36523.5 ns	38304 ns	0.95
array/reductions/reduce/Float32/dims=1	42444 ns	42098.5 ns	1.01
array/reductions/reduce/Float32/dims=2	59795 ns	60277 ns	0.99
array/reductions/reduce/Float32/dims=1L	52444 ns	52722 ns	0.99
array/reductions/reduce/Float32/dims=2L	71866 ns	72438 ns	0.99
array/reductions/mapreduce/Int64/1d	43028 ns	43667 ns	0.99
array/reductions/mapreduce/Int64/dims=1	44303 ns	45026 ns	0.98
array/reductions/mapreduce/Int64/dims=2	61445 ns	62167 ns	0.99
array/reductions/mapreduce/Int64/dims=1L	89047 ns	89081 ns	1.00
array/reductions/mapreduce/Int64/dims=2L	87991 ns	88471 ns	0.99
array/reductions/mapreduce/Float32/1d	36026 ns	38399 ns	0.94
array/reductions/mapreduce/Float32/dims=1	51970 ns	41819.5 ns	1.24
array/reductions/mapreduce/Float32/dims=2	59669 ns	60230.5 ns	0.99
array/reductions/mapreduce/Float32/dims=1L	52620 ns	52828 ns	1.00
array/reductions/mapreduce/Float32/dims=2L	72070 ns	72045.5 ns	1.00
array/broadcast	19836 ns	20443 ns	0.97
array/copyto!/gpu_to_gpu	12834 ns	13022 ns	0.99
array/copyto!/cpu_to_gpu	214812 ns	216732 ns	0.99
array/copyto!/gpu_to_cpu	287929 ns	283462 ns	1.02
array/accumulate/Int64/1d	124865 ns	124912 ns	1.00
array/accumulate/Int64/dims=1	83675 ns	84105 ns	0.99
array/accumulate/Int64/dims=2	158830 ns	158348 ns	1.00
array/accumulate/Int64/dims=1L	1710434 ns	1710807 ns	1.00
array/accumulate/Int64/dims=2L	966856.5 ns	966629 ns	1.00
array/accumulate/Float32/1d	108995 ns	109358 ns	1.00
array/accumulate/Float32/dims=1	80096 ns	80805 ns	0.99
array/accumulate/Float32/dims=2	147591 ns	148060.5 ns	1.00
array/accumulate/Float32/dims=1L	1619034 ns	1619572.5 ns	1.00
array/accumulate/Float32/dims=2L	697841 ns	698871 ns	1.00
array/construct	1287.4 ns	1243.1 ns	1.04
array/random/randn/Float32	46521.5 ns	48790 ns	0.95
array/random/randn!/Float32	24942 ns	25388 ns	0.98
array/random/rand!/Int64	27269 ns	27419 ns	0.99
array/random/rand!/Float32	8736.333333333334 ns	9072.666666666666 ns	0.96
array/random/rand/Int64	29927 ns	29902 ns	1.00
array/random/rand/Float32	13355 ns	13324 ns	1.00
array/permutedims/4d	54940 ns	57303.5 ns	0.96
array/permutedims/2d	54110 ns	54025.5 ns	1.00
array/permutedims/3d	54939.5 ns	55054.5 ns	1.00
array/sorting/1d	2758522 ns	2759246 ns	1.00
array/sorting/by	3345836 ns	3345927 ns	1.00
array/sorting/2d	1080524 ns	1082310 ns	1.00
cuda/synchronization/stream/auto	1041.9 ns	1045.9 ns	1.00
cuda/synchronization/stream/nonblocking	7703.5 ns	7338.6 ns	1.05
cuda/synchronization/stream/blocking	836.9480519480519 ns	812.9347826086956 ns	1.03
cuda/synchronization/context/auto	1193.2 ns	1170.7 ns	1.02
cuda/synchronization/context/nonblocking	7080.9 ns	7720.5 ns	0.92
cuda/synchronization/context/blocking	933.8857142857142 ns	910.6818181818181 ns	1.03

This comment was automatically generated by workflow using github-action-benchmark.