Stratified Sampling for Even Workload Partitioning Applied to IDA* and Delaunay Algorithms

This work presents Workload Partitioning and Scheduling (WPS), a novel algorithm for evenly partitioning the computational workload of large implicitly-defined work-list-based applications on distributed/shared-memory systems. In WPS, a stratified sampling technique estimates the number of work items that will be processed in each step of the target application. Then WPS uses this estimation to evenly partition and distribute the computational workload. An empirical evaluation on large applications -- Iterative-Deepening A* (IDA*) applied to (4 × 4)- and (5 × 5)-Sliding-Tile Puzzles, Delaunay Mesh Generation, and Delaunay Mesh Refinement -- shows that WPS is applicable to a range of applications. A coordination between WPS and existing work-stealing schedulers for intra-node load balancing yields additional speedups in the range of 18% to 40% compared to that achieved with the existing work-stealing schedulers alone. Such a coordination also outperforms an existing workload-partitioning scheme intended specifically for IDA* algorithms by 17% to 36%.