Stratified sampling for even workload partitioning

Abstract

This work presents a novel algorithm, Workload Partitioning and Scheduling (WPS), for evenly partitioning the computational workload of large implicitly-defined work-list based applications on distributed/shared-memory systems. WPS uses stratified sampling to estimate the number of work items that will be processed in each step of an application. WPS uses such estimation to evenly partition and distribute the computational workload. An empirical evaluation on large applications — Iterative-Deepening A∗ (IDA∗) applied to (4×4)-Sliding-Tile Puzzles, Delaunay Mesh Generation, and Delaunay Mesh Refinement — shows that WPS is applicable to a range of problems, and yields 28% to 49% speedups over existing work-stealing schedulers alone.