Single-ISA Asymmetric Multicore (AMC) architectures have shown high performance as well as power efficiency. However, current parallel programming environments do not perform well on AMC because they are designed for symmetric multicore architectures in which all cores provide equal performance. Their random task scheduling policies can result in unbalanced workloads in AMC and severely degrade the performance of parallel applications. To balance the workloads of parallel applications in AMC, this article proposes an adaptive Workload-Aware Task Scheduler (WATS) that consists of a history-based task allocator and a preference-based task scheduler. The history-based task allocator is based on a near-optimal, static task allocation using the historical statistics collected during the execution of a parallel application. The preference-based task scheduler, which schedules tasks based on a preference list, can dynamically adjust the workloads in AMC if the task allocation is less optimal due to approximation in the history-based task allocator. Experimental results show that WATS can improve both the performance and energy efficiency of task-based applications, with the performance gain up to 66.1% compared with traditional task schedulers. This article is extended from our previous conference paper, WATS: Workload-Aware Task Scheduling in Asymmetric Multicore Architecture [Chen et al. 2012a] , which was published in IPDPS 2012. The 30% new material comes from the following aspects. (1) In the previous paper, WATS relied on a strong assumption that the percentage of tasks executing the same function among all tasks is almost the same during the execution of a parallel application. For batch-based programs, WATS in this article does not rely on this strong assumption; (2) We have updated WATS for supporting the generalized scenario without the aforementioned strong assumption. In addition, the new history-based task allocator does not rely on the frequencies of cores any more; thus, WATS can work in all single-ISA multicore architectures; (3) This article has also significantly enhanced the experimental evaluation. We have evaluated both the performance and the energy efficiency of WATS on much more asymmetric architectures.
Q. Chen and M. Guo have presented an adaptive scheme of task scheduling on the basis of workloads for asymmetric multicore architectures REF .
Adaptive workload-aware task scheduling for single-ISA asymmetric multicore architectures
TACO
Computer Science