Rationalize and try to fix failing ldiv tests

[kshyatt]

Trying to fix intermittently failing CI. Doesn't make sense to have these checks for only one of the inplace/not-inplace versions. Hopefully this helps stability.

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diff --git a/test/libraries/cusparse/interfaces.jl b/test/libraries/cusparse/interfaces.jl
index fa25d8330..34f9d75f8 100644
--- a/test/libraries/cusparse/interfaces.jl
+++ b/test/libraries/cusparse/interfaces.jl
@@ -258,7 +258,7 @@ nB = 2
                                 end
                             end
                             @testset "\\ -- CuMatrix" begin
-                                C  = triangle(opa(A)) \ opb(B)
+                                C = triangle(opa(A)) \ opb(B)
                                 dC = triangle(opa(dA)) \ opb(dB)
                                 @test C ≈ collect(dC)
                                 if CUSPARSE.version() < v"12.0"

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CUDA.jl Benchmarks

Benchmark suite	Current: 2b58bc1	Previous: 4f38802	Ratio
latency/precompile	42937214294 ns	42824416725 ns	1.00
latency/ttfp	7006899212 ns	7051266950 ns	0.99
latency/import	3598749321 ns	3574411987 ns	1.01
integration/volumerhs	9609056 ns	9608389 ns	1.00
integration/byval/slices=1	146856 ns	146872 ns	1.00
integration/byval/slices=3	426026.5 ns	425794 ns	1.00
integration/byval/reference	145080 ns	144942 ns	1.00
integration/byval/slices=2	286326 ns	286144 ns	1.00
integration/cudadevrt	103529 ns	103388 ns	1.00
kernel/indexing	14148 ns	14276 ns	0.99
kernel/indexing_checked	15036 ns	15083 ns	1.00
kernel/occupancy	673.5796178343949 ns	677.6114649681529 ns	0.99
kernel/launch	2258.777777777778 ns	2157.8888888888887 ns	1.05
kernel/rand	14983 ns	14900 ns	1.01
array/reverse/1d	19640 ns	20028 ns	0.98
array/reverse/2d	23399 ns	25007 ns	0.94
array/reverse/1d_inplace	10382 ns	10952 ns	0.95
array/reverse/2d_inplace	12031 ns	12545 ns	0.96
array/copy	20933 ns	21084 ns	0.99
array/iteration/findall/int	157263 ns	158043.5 ns	1.00
array/iteration/findall/bool	139616 ns	140007 ns	1.00
array/iteration/findfirst/int	167118 ns	164557.5 ns	1.02
array/iteration/findfirst/bool	170423 ns	167385 ns	1.02
array/iteration/scalar	71422 ns	74295 ns	0.96
array/iteration/logical	213235 ns	215875.5 ns	0.99
array/iteration/findmin/1d	45880 ns	47331 ns	0.97
array/iteration/findmin/2d	96439 ns	97017 ns	0.99
array/reductions/reduce/Int64/1d	42507 ns	43072.5 ns	0.99
array/reductions/reduce/Int64/dims=1	45746.5 ns	55698.5 ns	0.82
array/reductions/reduce/Int64/dims=2	62141 ns	62572.5 ns	0.99
array/reductions/reduce/Int64/dims=1L	88721 ns	89129 ns	1.00
array/reductions/reduce/Int64/dims=2L	86683 ns	88184.5 ns	0.98
array/reductions/reduce/Float32/1d	34086 ns	35313 ns	0.97
array/reductions/reduce/Float32/dims=1	43953 ns	51818 ns	0.85
array/reductions/reduce/Float32/dims=2	59614 ns	59835 ns	1.00
array/reductions/reduce/Float32/dims=1L	52197.5 ns	52336 ns	1.00
array/reductions/reduce/Float32/dims=2L	69873 ns	70233.5 ns	0.99
array/reductions/mapreduce/Int64/1d	42098 ns	44093 ns	0.95
array/reductions/mapreduce/Int64/dims=1	46766 ns	47633.5 ns	0.98
array/reductions/mapreduce/Int64/dims=2	61726 ns	62709 ns	0.98
array/reductions/mapreduce/Int64/dims=1L	88921 ns	89036 ns	1.00
array/reductions/mapreduce/Int64/dims=2L	86327 ns	87347.5 ns	0.99
array/reductions/mapreduce/Float32/1d	34357.5 ns	34780.5 ns	0.99
array/reductions/mapreduce/Float32/dims=1	41477 ns	41996.5 ns	0.99
array/reductions/mapreduce/Float32/dims=2	59999 ns	60450.5 ns	0.99
array/reductions/mapreduce/Float32/dims=1L	52679 ns	52739 ns	1.00
array/reductions/mapreduce/Float32/dims=2L	70249.5 ns	70715 ns	0.99
array/broadcast	20011 ns	20360 ns	0.98
array/copyto!/gpu_to_gpu	12766 ns	12890 ns	0.99
array/copyto!/cpu_to_gpu	213580.5 ns	217680 ns	0.98
array/copyto!/gpu_to_cpu	283317 ns	286671 ns	0.99
array/accumulate/Int64/1d	124768.5 ns	125190 ns	1.00
array/accumulate/Int64/dims=1	83243 ns	84136 ns	0.99
array/accumulate/Int64/dims=2	157572 ns	158690 ns	0.99
array/accumulate/Int64/dims=1L	1708962 ns	1709534 ns	1.00
array/accumulate/Int64/dims=2L	965903 ns	967437 ns	1.00
array/accumulate/Float32/1d	109074 ns	109803 ns	0.99
array/accumulate/Float32/dims=1	80983 ns	81170 ns	1.00
array/accumulate/Float32/dims=2	147799 ns	147834 ns	1.00
array/accumulate/Float32/dims=1L	1619103.5 ns	1619112.5 ns	1.00
array/accumulate/Float32/dims=2L	698281 ns	698583 ns	1.00
array/construct	1292.3 ns	1275.8 ns	1.01
array/random/randn/Float32	43716.5 ns	44761 ns	0.98
array/random/randn!/Float32	25068 ns	25104 ns	1.00
array/random/rand!/Int64	27432 ns	27468 ns	1.00
array/random/rand!/Float32	8771 ns	8662 ns	1.01
array/random/rand/Int64	38026 ns	30080 ns	1.26
array/random/rand/Float32	12913 ns	13152 ns	0.98
array/permutedims/4d	60201 ns	60473 ns	1.00
array/permutedims/2d	53933 ns	54524 ns	0.99
array/permutedims/3d	54865.5 ns	55468 ns	0.99
array/sorting/1d	2756471 ns	2763710 ns	1.00
array/sorting/by	3366837 ns	3356377 ns	1.00
array/sorting/2d	1087589 ns	1085339 ns	1.00
cuda/synchronization/stream/auto	1032.8 ns	1018.0909090909091 ns	1.01
cuda/synchronization/stream/nonblocking	7152.2 ns	7602.700000000001 ns	0.94
cuda/synchronization/stream/blocking	810.1630434782609 ns	806.236559139785 ns	1.00
cuda/synchronization/context/auto	1178.4 ns	1183.8 ns	1.00
cuda/synchronization/context/nonblocking	7946.4 ns	7801 ns	1.02
cuda/synchronization/context/blocking	918.8809523809524 ns	897.2923076923076 ns	1.02

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

[kshyatt]

@maleadt failing tests are the CUSTATEVEC ones, can we merge?