Fundamentals algorithms for SIMD and MIMD hypercubes are developed.
These include algorithms for such problems as data broadcasting, data
sum, prefix sum, shift, data circulation, data accumulation, sorting,
random access reads and writes and data permutation. The fundamental
algorithms are then used to obtain efficient hypercube algorithms for
matrix multiplication, image processing problems such as convolution,
template matching, hough transform, clustering and image processing
transformation, and string editing. Most of the algorithms in this book
are for hypercubes with the number of processors being a function of
problems size. However, for image processing problems, the book also
includes algorithms for and MIMD hypercube with a small number of
processes. Experimental results on an NCUBE/77 MIMD hypercube are also
presented. The book is suitable for use in a one-semester or one-quarter
course on hypercube algorithms. For students with no prior exposure to
parallel algorithms, it is recommended that one week will be spent on
the material in chapter 1, about six weeks on chapter 2 and one week on
chapter 3. The remainder of the term can be spent covering topics from
the rest of the book.