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Each matrix-matrix multiplication requires three matrices (two input matrices and one result matrix) in memory during execution.
We list two measures of runtime since our algorithm (and also NISE) implemented in MATLAB directly uses a processed matrix in memory as its input.
However, UPGMA requires the entire dissimilarity matrix in memory.
The problem however, is that UPGMA (or e.g. complete-linkage) requires the entire similarity matrix in memory.
Importantly, we do not keep the full distance matrix in memory using a special FastMap trick explained later in the paper.
For such n, pure distance-based methods which need to keep the full distance matrix in memory become inappropriate in contrast to our methods which are linear in n in memory space.
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In particular we analyze the effect of novel data layouts which, for example, interleave the matrices in memory to aid vectorization and prefetching of data.
It becomes infeasible to store this matrix in the memory for large values of n.
This signal is digitized and the image is stored as a matrix in computer memory.
The compact (mathbf {S} -matrix is arranged in memory such that the fastest changing dimension, considered to be the x-axis, lies along the direction of the different plane waves.
They receive all data of image from Sender Federate in the same way the other Federates, then they build the image matrix in the device memory and an OpenCL kernel is initialized.
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