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docs/src/tutorials/accelerating_choices.md

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Great, you've learned how to use LinearSolve.jl and you're using it daily,
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either directly or through other SciML libraries, and you want to improve
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your performance. How can this be done? While it might seem at first like a
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hopeless endevour, "A\b uses a BLAS library and so it's already highly optimized
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hopeless endeavour, "A\b uses a BLAS library and so it's already highly optimized
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C code", it turns out there are many factors you need to consider to squeeze out
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the last 10x of performance. And yes, it can be about a factor of 10 in some
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scenarios, so let's dive in.

src/KLU/klu.jl

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@@ -57,7 +57,7 @@ function kluerror(status::Integer)
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elseif status == KLU_INVALID
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throw(ArgumentError("Invalid Status"))
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elseif status == KLU_TOO_LARGE
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throw(OverflowError("Integer overflow has occured"))
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throw(OverflowError("Integer overflow has occurred"))
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else
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throw(ErrorException("Unknown KLU error code: $status"))
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end

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