Efficient application scheduling is critical for achieving high performance in heterogeneous computing systems. This problem has proved to be NP-complete even for the homogeneous case, heading research efforts in obtaining low complexity heuristics that produce good quality schedules. Such an example is HEFT, one of the most efficient list scheduling heuristics in terms of makespan and robustness. In this paper, we propose two task scheduling methods for heterogeneous computing systems that can be integrated to several task scheduling algorithms. First, a method that improves the scheduling time (the time for obtaining the output schedule) of a family of task scheduling algorithms is delivered without sacrificing the schedule length, when the computation costs of the application tasks are unknown. Second, a method that improves the scheduling length (makespan) of several task scheduling algorithms is proposed, by identifying which tasks are going to be executed as single-threaded and which as multi-threaded implementations, as well as the number of the threads used. We showcase both methods by using HEFT popular algorithm, but they can be integrated to other algorithms too, such as HCPT, HPS, PETS and CPOP. The experimental results, which consider 14580 random synthetic graphs and five real world applications, show that by enhancing HEFT algorithm with the two proposed methods, significant makespan gains and high scheduling time gains, are achieved.



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Journal of Parallel and Distributed Computing (Elsevier)



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School of Engineering, Computing and Mathematics