Optimizing layer‐based scheduling algorithms for parallel tasks with dependencies

Abstract

SUMMARYProgramming with parallel tasks leads to task graphs with dependencies representing a parallel program. Scheduling algorithms are employed to find an efficient execution order of the parallel tasks. A large variety of scheduling algorithms exist, including layer-based scheduling algorithms for homogeneous target platforms that build consecutive layers of independent parallel tasks and schedule each layer separately. Although these scheduling algorithms provide good results in terms of scheduling algorithm runtime and schedule execution time, the resulting schedules leave room for optimization. This article proposes an optimization for arbitrary layer-based scheduling algorithms, which is called Move-blocks algorithm. Given a layer-based schedule of the parallel tasks, this algorithm moves blocks of parallel tasks into preceding layers in order to reduce the overall execution time of a task-based application. Suitable blocks of parallel tasks are identified by the algorithm Find-blocks, which is employed together with the Move-blocks algorithm. The algorithm Move-blocks is applied to four well-known scheduling algorithms. A detailed evaluation for a wide range of test cases is given.

Modeling and analyzing the energy consumption of fork‐join‐based task parallel programs

“…The time M.1/ is determined by the processor P e that finishes its work last. An experimental comparison of this greedy scheduling algorithm with other scheduling algorithms using the overall execution time (makespan) as objective function is included in [21,22]. From an energy-consumption perspective, the resulting schedule can be improved by applying the results from Section 4:…”

“…In the following, we describe scheduling experiments for randomly generated task sets and consider the resulting energy consumption. For a comparison with other scheduling algorithms using the makespan as objective function, we refer to [21,22]. Figure 8 illustrates the result of the scheduling algorithms for the scheduling of 20 (left), 50 (middle), and 100 (right) tasks executed on 10 processors.…”