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what is the current state of the art in fast and parallel matrix in-place transposition?

I would be very happy, if I could be given some pseudocode for this problem. As far as I could find papers, they do not include very detailed descriptions.

Thanks!

PS: Of course, we do not restrict to square matrices, but rather arbitrary rectangular matrices. Their memory layout is usually a linear sequence of entries in the memory (say, as in C) and it is a highly non-trivial task to convert this data block in-place/in-situ to the sequence that corresponds to the transposed matrix.

Some information can be found in this wikipedia article, whose references I did not find to be very helpful though: Wikipedia Entry.

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    $\begingroup$ I probably miss something, but what's wrong with running ${n \choose 2}$ processes in parallel for each $0\le i<j<n$ to swap $A_{ij}$ with $A_{ji}$? $\endgroup$ Nov 25, 2011 at 13:22
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    $\begingroup$ @RaduGRIGore: Not optimal, if you take into account the memory hierarchy. (Some keywords: external memory model; I/O model; cache-oblivious algorithms.) $\endgroup$ Nov 25, 2011 at 17:26
  • $\begingroup$ @Radu: This only workds with square matrices, does it? Otherwise please tell me about that algorithm. $\endgroup$
    – shuhalo
    Nov 26, 2011 at 13:12
  • $\begingroup$ I would break the problem into two subproblems: (1) What is the cycle decomposition of the permutation which corresponds to in-place matrix transposition? This part is discussed in Wikipedia. (2) How do we implement a cycle in parallel? This part sounds like it should be studied very well, although I am not an expert and I do not know any reference. $\endgroup$ Nov 26, 2011 at 15:23
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    $\begingroup$ Why transpose? Just return $i,j$ th element when asked for $j,i$ th element! ;-) $\endgroup$ Nov 26, 2011 at 15:36

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Sorry, no pseudocode, but a recent paper with a fast parallel algorithm, to appear in ACM Transactions on Mathematical Software:

@Article{Gustavson:2011:PCE, author = "Fred Gustavson and Lars Karlsson and Bo K{\aa}gstr{\"o}m", title = "Parallel and Cache-Efficient In-Place Matrix Storage Format Conversion", journal = "{ACM} Transactions on Mathematical Software", accepted = "8 July 2010", upcoming = "true", abstract = " Techniques and algorithms for efficient in-place conversion to and from standard and blocked matrix storage formats are described. Such functionality is required by numerical libraries that use different data layouts internally. Parallel algorithms and a software package for in-place matrix storage format conversion based on in-place matrix transposition are presented and evaluated. A new algorithm for in-place transposition which efficiently determines the structure of the transposition permutation a priori is one of the key ingredients. It enables effective load balancing in a parallel environment.", }

Here is the journal page, and here is a pdf version of the paper. I hope this can be useful.

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You could look at this paper, which gives a proposal for how to solve this problem for a practical application in radio astronomy. One of the authors is quite knowledgeable about computer science, so they're not rediscovering the wheel here.

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  • $\begingroup$ Interesting paper, even though it deals with a slightly different version of the problem: transposing a matrix through an out of core, hardware implementation using FPGA etc. Thank you for sharing this. $\endgroup$ Dec 1, 2011 at 14:11
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Load two blocks that fit in cache then write them back out transposed? Depends on how they are stored if you want a parallelization.

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