Low-Cost Task Scheduling for Distributed-Memory Machines

ÐIn compile-time task scheduling for distributed-memory systems, list scheduling is generally accepted as an attractive approach since it pairs low cost with good results. List scheduling algorithms schedule tasks in order of their priority. This priority can be computed either 1) statically, before the scheduling, or 2) dynamically, during the scheduling. In this paper, we show that list scheduling with statically computed priorities can be performed at a significantly lower cost than existing approaches, without sacrificing performance. Our approach is general, i.e., it can be applied to any list scheduling algorithm with static priorities. The lowcomplexity is achieved by using low-complexity methods for the most time consuming parts in list scheduling algorithms, i.e., processor selection and task selection, preserving the criteria used in the original algorithms. We exemplify our method by applying it to the MCP algorithm. Using an extension of this method, we can also reduce the time complexity of a particular class of list scheduling with dynamic priorities (including algorithms such as DLS, ETF, or ERT). Our results confirm that the modified versions of the list scheduling algorithms obtain a performance comparable to their original versions, yet at a significantly lower cost. We also show that the modified versions of the list scheduling algorithms consistently outperform multistep algorithms, such as DSC-LLB, which also have higher complexity and clearly outperform algorithms in the same class of complexity, such as CPM. Index TermsÐCompile-time task scheduling, list scheduling, dataflow graphs, distributed-memory multiprocessors.