Good load balancing is the key to deriving maximal performance from multiprocessors. Several successful dynamic load balancing techniques on tightly-coupled multiprocessors have been developed. However, load balancing is more difficult on loosely-coupled multiprocessors because inter-processor communication overheads cost more. Dynamic load balancing techniques have been employed in a few programs on loosely-coupled multiprocessors, but they are tightly built into the particular programs and not much attention is paid to scalability. We have developed a dynamic load balancing scheme which is applicable to OR-parallel programs in general. Processors are grouped, and work loads of groups and processors are balanced hierarchically. Moreover, it is scalable to any number of processors because of this multi-level hierarchical structure. The scheme is tested for the all-solution exhaustive search Pentomino program on the mesh-connected loosely-coupled multiprocessor Multi-PSI, and speedups of 28.4 times with 32 processors and 50 times with 64 processors have been attained.

Granularity collecting (e.g., in OR-parallel Prolog) and removal of synchronization points (e.g., in AND-parallel committed-choice languages), are in general useful techniques for speeding up parallel logic programs. In this paper we explain these techniques with a unifying example: the Semigroup Problem, the calculation of the members of a semigroup given an initial set of generators. Performance measurements on a Sequent Symmetry multiprocessor are presented as evidence of the utility of the paradigms.