Scheduling optimization through iterative refinement

Scheduling DAGs with communication times is the theoretical basis for achieving efficient parallelism on distributed memory systems. We generalize Graham's task-level in a manner to incorporate the effects of computation, data size, and network latency. A new scheduling that uses the proposed task-level to make early reservation of resources for critical computation and communication is proposed. We also propose an optimization called Iterative Refinement Scheduling (IRS) that alternatively schedules the computation graph and its associated reverse. The task-level used in some scheduling iteration is the task's starting time that is achieved in the very previous iteration. IRS enables searching and optimizing solutions as the result of using more refined task-level in each scheduling iteration. Evaluation and analysis of the results are carried out for different instances of problem granularities, parallelism, and network latency such as the fully connected, hypercube, and r...

• Type: Journal
• Year: 2000
• Where: JSA
• Authors: Mayez A. Al-Mouhamed, Adel Al-Massarani
• Keywords: Iterative Refinement Scheduling, JSA 2000, Network Latency, Scheduling Iteration