2014 23rd International Conference on Parallel Architecture and Compilation (PACT) (2014)
Aug. 23, 2014 to Aug. 27, 2014
Jeeva Paudel , University of Alberta, Edmonton, Canada
Jose Nelson Amaral , University of Alberta, Edmonton, Canada
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.
Partitioning algorithms, Scheduling, Clustering algorithms, Processor scheduling, Programming, Load management
J. Paudel and J. N. Amaral, "Stratified sampling for even workload partitioning," 2014 23rd International Conference on Parallel Architecture and Compilation (PACT), Edmonton, Canada, 2014, pp. 503-504.