An efficient parallel-processing method for transposing large matrices in place

We have developed an efficient algorithm for transposing large matrices in place. The algorithm is efficient because data are accessed either sequentially in blocks or randomly within blocks small enough to fit in cache, and because the same indexing calculations are shared among identical procedure...

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Vydáno v:IEEE transactions on image processing Ročník 8; číslo 9; s. 1265 - 1275
Hlavní autor: Portnoff, M.R.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.09.1999
Institute of Electrical and Electronics Engineers
Témata:
Algorithms
Applied sciences
Computer architecture
Concurrent computing
Displays
Exact sciences and technology
Gates
Image processing
Indexing
Information, signal and communications theory
Mathematical analysis
Matrices
Matrix converters
Matrix methods
Microprocessors
Parallel processing
Random access memory
Read-write memory
Remote sensing
Signal processing
Telecommunications and information theory
Parallel processing
Image processing
Fast algorithm
Imaging
Synthetic aperture radar
ISSN:1057-7149
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Shrnutí:We have developed an efficient algorithm for transposing large matrices in place. The algorithm is efficient because data are accessed either sequentially in blocks or randomly within blocks small enough to fit in cache, and because the same indexing calculations are shared among identical procedures operating on independent subsets of the data. This inherent parallelism makes the method well suited for a multiprocessor computing environment. The algorithm is easy to implement because the same two procedures are applied to the data in various groupings to carry out the complete transpose operation. Using only a single processor, we have demonstrated nearly an order of magnitude increase in speed over the previously published algorithm by Gate and Twigg (1977) for transposing a large rectangular matrix in place. With multiple processors operating in parallel, the processing speed increases almost linearly with the number of processors. A simplified version of the algorithm for square matrices is presented as well as an extension for matrices large enough to require virtual memory.
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ISSN:1057-7149
DOI:10.1109/83.784438