An Efficient Mean Field Annealing Formulation for Mapping Unstructured Domains to Hypercubes

We propose an e cient MFA formulation for mapping un structured domains to hypercube connected distributed memory archi tectures In the general MFA formulation N P spin variables are main tained and an individual MFA iteration requires davgP P time for the mapping of a sparse domain graph withN vertices and average vertex degree of davg to a parallel architecture with P processors The proposed hypercube speci c MFA formulation asymptotically reduces the number of spin variables and the computational complexity of an individual MFA iteration to Nlg P and davglg P Plg P respectively by exploiting the topological properties of hypercubes