Profiler: relax parsing of color flags in marker data.

[maleadt]

Even though the flag enum supports explicitly indicating the color is not set, I guess it's also valid to provide no indication of the color; although it seems weird to me that both should be supported.

In any case, relaxed the parsing because @vchuravy actually reported encountering this:

┌ Error: Unexpected CUPTI marker color flag 0. Please file an issue.
└ @ CUDA.Profile ~/.julia/packages/CUDA/Il00B/src/profile.jl:596

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 90.42%. Comparing base (0b05451) to head (29a4d18).
⚠️ Report is 2 commits behind head on master.

Additional details and impacted files

@@           Coverage Diff           @@
##           master    #3075   +/-   ##
=======================================
  Coverage   90.42%   90.42%           
=======================================
  Files         141      141           
  Lines       11993    11993           
=======================================
  Hits        10845    10845           
  Misses       1148     1148           

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CUDA.jl Benchmarks

Details

Benchmark suite	Current: 29a4d18	Previous: 0b05451	Ratio
latency/precompile	4605232371 ns	4612556023 ns	1.00
latency/ttfp	4414186531.5 ns	4408562143 ns	1.00
latency/import	3826664571 ns	3821839641 ns	1.00
integration/volumerhs	9439705 ns	9448669.5 ns	1.00
integration/byval/slices=1	146084 ns	145839 ns	1.00
integration/byval/slices=3	423274 ns	423425 ns	1.00
integration/byval/reference	144230 ns	144031 ns	1.00
integration/byval/slices=2	284858 ns	284559 ns	1.00
integration/cudadevrt	102869 ns	102661 ns	1.00
kernel/indexing	13635 ns	13693 ns	1.00
kernel/indexing_checked	14420.5 ns	14309 ns	1.01
kernel/occupancy	698.658064516129 ns	663.8086419753087 ns	1.05
kernel/launch	2208.4444444444443 ns	2210.6666666666665 ns	1.00
kernel/rand	14756 ns	16005 ns	0.92
array/reverse/1d	18787.5 ns	18568 ns	1.01
array/reverse/2dL_inplace	66216 ns	66179 ns	1.00
array/reverse/1dL	69404 ns	69124 ns	1.00
array/reverse/2d	21174 ns	21081 ns	1.00
array/reverse/1d_inplace	10516.333333333334 ns	8671.333333333334 ns	1.21
array/reverse/2d_inplace	10621.5 ns	10405 ns	1.02
array/reverse/2dL	73531 ns	73273 ns	1.00
array/reverse/1dL_inplace	66197 ns	66158 ns	1.00
array/copy	18997.5 ns	18801 ns	1.01
array/iteration/findall/int	150723.5 ns	149677 ns	1.01
array/iteration/findall/bool	133364.5 ns	132815 ns	1.00
array/iteration/findfirst/int	85154 ns	84077 ns	1.01
array/iteration/findfirst/bool	83075 ns	81834 ns	1.02
array/iteration/scalar	69897 ns	67842 ns	1.03
array/iteration/logical	204503.5 ns	205668 ns	0.99
array/iteration/findmin/1d	89275 ns	88143.5 ns	1.01
array/iteration/findmin/2d	117807 ns	117602 ns	1.00
array/reductions/reduce/Int64/1d	44171 ns	43879 ns	1.01
array/reductions/reduce/Int64/dims=1	42699 ns	42363.5 ns	1.01
array/reductions/reduce/Int64/dims=2	59878 ns	59729 ns	1.00
array/reductions/reduce/Int64/dims=1L	88101 ns	87783 ns	1.00
array/reductions/reduce/Int64/dims=2L	85199 ns	84600.5 ns	1.01
array/reductions/reduce/Float32/1d	36297 ns	35554 ns	1.02
array/reductions/reduce/Float32/dims=1	40431.5 ns	40471.5 ns	1.00
array/reductions/reduce/Float32/dims=2	57268 ns	57348 ns	1.00
array/reductions/reduce/Float32/dims=1L	52335 ns	52155 ns	1.00
array/reductions/reduce/Float32/dims=2L	70371 ns	70051 ns	1.00
array/reductions/mapreduce/Int64/1d	44409 ns	43658 ns	1.02
array/reductions/mapreduce/Int64/dims=1	42847 ns	42850 ns	1.00
array/reductions/mapreduce/Int64/dims=2	60052 ns	60283 ns	1.00
array/reductions/mapreduce/Int64/dims=1L	88214 ns	87856 ns	1.00
array/reductions/mapreduce/Int64/dims=2L	85258 ns	84886 ns	1.00
array/reductions/mapreduce/Float32/1d	36210 ns	35439 ns	1.02
array/reductions/mapreduce/Float32/dims=1	40252.5 ns	49602 ns	0.81
array/reductions/mapreduce/Float32/dims=2	57304 ns	57112 ns	1.00
array/reductions/mapreduce/Float32/dims=1L	52092 ns	52066 ns	1.00
array/reductions/mapreduce/Float32/dims=2L	69839 ns	70220.5 ns	0.99
array/broadcast	20864 ns	20835 ns	1.00
array/copyto!/gpu_to_gpu	11655 ns	11409 ns	1.02
array/copyto!/cpu_to_gpu	218542 ns	217482 ns	1.00
array/copyto!/gpu_to_cpu	284489 ns	284547 ns	1.00
array/accumulate/Int64/1d	118991 ns	119536 ns	1.00
array/accumulate/Int64/dims=1	80432 ns	80581.5 ns	1.00
array/accumulate/Int64/dims=2	156582 ns	156671 ns	1.00
array/accumulate/Int64/dims=1L	1695513 ns	1706437 ns	0.99
array/accumulate/Int64/dims=2L	962297 ns	962104 ns	1.00
array/accumulate/Float32/1d	102099 ns	102057 ns	1.00
array/accumulate/Float32/dims=1	78136.5 ns	77830 ns	1.00
array/accumulate/Float32/dims=2	144751.5 ns	144868 ns	1.00
array/accumulate/Float32/dims=1L	1587486 ns	1586085 ns	1.00
array/accumulate/Float32/dims=2L	658744 ns	661758 ns	1.00
array/construct	1333.3 ns	1349.1 ns	0.99
array/random/randn/Float32	38775 ns	42622 ns	0.91
array/random/randn!/Float32	31534 ns	31841 ns	0.99
array/random/rand!/Int64	34323 ns	34412 ns	1.00
array/random/rand!/Float32	8716.666666666666 ns	8601 ns	1.01
array/random/rand/Int64	34806 ns	37538 ns	0.93
array/random/rand/Float32	13272.5 ns	13018 ns	1.02
array/permutedims/4d	52392.5 ns	52619.5 ns	1.00
array/permutedims/2d	52667 ns	52940 ns	0.99
array/permutedims/3d	52914 ns	53310 ns	0.99
array/sorting/1d	2736370 ns	2735465.5 ns	1.00
array/sorting/by	3305502.5 ns	3315555 ns	1.00
array/sorting/2d	1070338 ns	1072718 ns	1.00
cuda/synchronization/stream/auto	1059.7 ns	1042.1 ns	1.02
cuda/synchronization/stream/nonblocking	7939 ns	7867.299999999999 ns	1.01
cuda/synchronization/stream/blocking	873.95 ns	828.4299065420561 ns	1.05
cuda/synchronization/context/auto	1214.3 ns	1198.8 ns	1.01
cuda/synchronization/context/nonblocking	7457.1 ns	7295 ns	1.02
cuda/synchronization/context/blocking	929.4324324324324 ns	929.6410256410256 ns	1.00

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