Workflow Simulation Aware and Multi-threading Effective Task Scheduling for Heterogeneous Computing

Efficient application scheduling is critical for achieving high performance in heterogeneous computing systems. This problem has proved to be NP-complete, heading research efforts in obtaining low complexity heuristics that produce good quality schedules. Although this problem has been extensively studied in the past, all the related works assume the computation costs of application tasks on processors are available a priori, ignoring the fact that the time needed to run/simulate all these tasks is orders of magnitude higher than finding a good quality schedule, especially in heterogeneous systems. In this paper, we propose two new methods applicable to several task scheduling algorithms for heterogeneous computing systems. We showcase both methods by using HEFT well known and popular algorithm, but they are applicable to other algorithms too, such as HCPT, HPS, PETS and CPOP. First, we propose a methodology to reduce the scheduling time of HEFT when the computation costs are unknown, without sacrificing the length of the output schedule (monotonic computation costs); this is achieved by reducing the number of computation costs required by HEFT and as a consequence the number of simulations applied. Second, we give heuristics to find which tasks are going to be executed as Single-Thread and which as Multi-Thread CPU implementations, as well as the number of the threads used. The experimental results considering both random graphs and real world applications show that extending HEFT with the two proposed methods achieves better schedule lengths, while at the same time requires from 4.5 up to 24 less simulations.