Load balancing strategies for symbolic vision computations

Most intermediate and high-level vision algorithms manipulate symbolic features. A key operation in these vision algorithms is to search symbolic features satisfying certain geometric constraints. Parallelizing this symbolic search needs a non-trivial algorithmic technique due to the unpredictable workload. In this paper, we propose load balancing strategies for parallelizing symbolic search operations on distributed memory machines. By using an initial workload estimate, we rst partition the computations such that the workload is distributed evenly across the processors. In addition, we perform task migrations dynamically to adapt to the evolving workload. To demonstrate the usefulness of our load balancing strategies, experiments were conducted on an IBM SP2 and a Cray T3D. Our results show that our task migration strategy can balance the unpredictable workload with little overhead. Our code using C and MPI is portable onto other high performance computing platforms.