Michael R. Portnoff

Learn More
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(More)
An attempt is made to transpose an arbitrary matrix when the total number of matrix elements is too large to store them all in random-access memory. This problem is often a computational bottleneck in large computed-imaging problems. A simple algorithm for obtaining the transposed matrix using only two read/write passes over the data is derived. This(More)
  • 1