  Parallel implementations of distributed global optimization (DGO) [13] on
MP-1 and NCUBE parallel computers revealed an approximate O(n) increase in the
performance of this algorithm. Therefore, the implementation of the DGO on
parallel processors can remedy the only draw back of this algorithm which is
the O(n2) of execution time as the number of the dimensions increase. The speed
up factor of the parallel implementations of DGO is measured with respect to
the sequential execution time of the identical problem on SPARC IV computer.
The best speed up was achieved by the SIMD implementation of the algorithm on
the MP-1 with the total speedup of 126 for an optimization problem with n = 9.
This optimization problem was distributed across 128 PEs of Mas-Par.
