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In addition, this row-wise decomposition provides plenty of alternatives in data transpose, and different transpose order results in different amounts of communication.
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The two major differences between this implementation and that for a general sparse linear solver are (1) a communication-efficient approach to handle data expansion and truncation and data transpose simultaneously; (2) the interleaving of matrix-vector multiplications and vector inner product calculations to reduce synchronization cost and latency.
mathrm{Final}kern0.5em mathrm{F}mathrm{eature}kern0.5em mathrm{R}mathrm{ern0.5em =kern0.5em mathrm{R}mathrm{F}mathrm{E}ast mathrm{R}mathrm{D}mathrm{M} (4 where RFE is the matrix with the eigenvectors in the columns transposed and RDM is the matrix mean-adjusted data transposed.
Subsequently, the data matrix was transposed and clustering of variables performed in "R" mode (See details in Legendre and Legendre [ 54]).
and are transpose and complex conjugate transpose.
The, represent the transpose and the transpose conjugate.
"T" and "H" are the matrix transpose and Hermitian transpose.
T and H represent the transpose and Hermitian transpose, respectively.
Superscripts and stand for transpose and conjugate transpose, respectively.
T and H denote transpose and Hermitian transpose, respectively.
The superscripts and denote transpose and Hermitian transpose, respectively.
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