A scheduling system for exploiting data and task parallelism on PC laboratory clusters

Presents a framework for deciding on a good execution strategy for a given program based on the available data and task parallelism in the program on PC laboratory clusters. Proposes a virtual cluster scheduling scheme to take account of the relationships between tasks for task parallelism, and also processor speed, processor load and network environment to balance load for data parallelism in a PC cluster environment. The approach is very effective in terms of the overall execution time, and demonstrates the feasibility of automatic cluster assignment, processor set selection and data partition functions for data and task parallel programs.