| Abstract: | Ordering clones from a genomic library into physical maps of whole chromosomes presents a pivotal computational problem in genetics. Previous research has shown the physical mapping problem to be isomorphic to the NP-complete Optimal Linear Arrangement (OLA) problem for which no polynomial-time algorithm for determining the optimal solution is known. Serial implementations of stochastic global optimization techniques such as simulated annealing yielded very good results but proved computationally intensive. The design, analysis and implementation of coarse-grained parallel MIMD algorithms for simulated annealing on the Intel iPSC/860 hypercube is presented. Data decomposition and control decomposition strategies based on Markov chain decomposition, perturbation methods and problem-specific annealing heuristics are proposed and applied to the physical mapping problem. A suite of parallel algorithms are implemented on an 8-node Intel iPSC/860 hypercube, exploiting the nearest-neighbor communication pattern on the Boolean hypercube topology. Convergence, speedup and scalability characteristics of the various parallel algorithms are analyzed and discussed. Results indicate a deterioration of performance when a single Markov chain of solution states is distributed across multiple processing elements in the Intel iPSC/860 hypercube. |
