If you call a library like LAPACK or BLAS (which are written in FORTRAN and use column major order) from a C-like language that uses row major order, won't you lose performance and use a lot of memory due to the creation of transposed matrices? Tests are best, of course, but can anyone with experience tell me whether optimized libraries still improve performance, even after that overhead?
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$\begingroup$ I haven't tested the performance penalty, but you might use a library or a toolkit that hides away memory layout. E.g. in NumPy (relevant parts written in C), you can choose the memory layout on array object creation. $\endgroup$– AlexEAug 12, 2014 at 7:00
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$\begingroup$ If I am not mistaken, BLAS and LAPACK have a "transpose" argument that tells if you want the operation done with $A$ or with $A^T$. So you can effectively pass matrices to BLAS either in row-major or column-major order, you just have to specify it. No explicit transpositions are performed. Of course, some algorithms might perform better with row-major and some with column-major storage, but this seems a different issue. $\endgroup$– Federico PoloniAug 12, 2014 at 12:38
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Many of these libraries have C interfaces that swap the meaning of the ordering internally without swapping the data. Also, using C-style double pointers for 2-D matrices is probably the wrong choice in the first place (due to the double lookup), so you can make your data appear column-major by indexing into a linear C array or use libraries that work around these issues.
answered Aug 12, 2014 at 11:58
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$\begingroup$ It turned out that the LAPACK port I was using, CLAPACK, didn't have that option but a more recent one called LAPACKE did. $\endgroup$– EmreAug 14, 2014 at 22:25
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You can use the identity C^T = (AB)^T = B^T A^T to use BLAS to compute on row-major arrays from C.
In any case, the overhead of transposition is usually not a bottleneck compared to O(N^3) BLAS3 or LAPACK routines, but I should note that I am oft to point out counterexamples from my own research.
