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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.
Original file line number Diff line number Diff line change @@ -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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