Research on VLSI placement has extended the standard sequential simulated annealing technique to two multiprocessing variants. In one technique, processors perform moves on disjoint partitions of locally-stored circuit grids. In the other, processors perform simultaneous moves on a shared grid. Our research explores new techniques in the first category-called spatial decomposition algorithms. We describe the impact of cell mobility and cost-function errors in parallel simulated annealing. We show that changing the partition shape can affect these measures, and the quality of the final result. We also show a trade-off: execution speed vs. increased cell mobility and decreased cost-function errors. We present four rectangular decomposition methods. Using two circuit examples, we compare their convergence properties to that of a "standard" random spatial decomposition technique. Runs were performed in a simulated RP3 environment. One method we developed, "sharp random rectangles," converged better than the other techniques we studied. On one example, sharp random rectangles on 8 processors converged better than standard sequential algorithms. This promising technique allows us to increase the stream-length, and thereby reduce execution time. The authors continue their research to better quantify cell mobility and cost-function errors, and to run the rectangular algorithms on other types of multiprocessors to obtain speed-up information.