Leveraging Data-Flow Task Parallelism for Locality-Aware Dynamic Scheduling on Heterogeneous Platforms

Simsek, Osman Seckin; Drebes, Andi; Pop, Antoniu

doi:10.1109/ipdpsw.2018.00093

Cited by 1 publication

(1 citation statement)

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“…Dynamic scheduling aims to effectively partition work across devices during execution, which has attracted more and more attentions recently. Many researches have concentrated on dynamic scheduling strategies designed for taskparallel applications, such as work-stealing scheduling [18], speedup-based scheduling [19], locality-aware scheduling [20], feature-aware scheduling [21], load-aware scheduling [22], energy-aware scheduling [23]. Recently some dynamic scheduling strategies designed for data-parallel applications have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

Efficient Inter-Device Task Scheduling Schemes for Multi-Device Co-Processing of Data-Parallel Kernels on Heterogeneous Systems

2021

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Heterogeneous systems consisting of multiple multi-core CPUs and many-core accelerators have recently come into wide use, and more and more parallel applications are developed in such a heterogeneous system. To fully utilize multiple compute devices to cooperatively and concurrently execute data-parallel kernels on heterogeneous systems, a feedback-based dynamic and elastic task scheduling scheme is proposed, which can provide a better load balance, a greater device utilization, and a lower scheduling overhead by flexibly and dynamically adjusting the workload between devices during execution. The proposed method is more suitable for data-parallel kernels whose computation and data are uniformly distributed, but is less suitable for data-parallel kernels whose computation and data are non-uniformly distributed. Thus, an asynchronous-based dynamic and elastic task scheduling scheme is proposed, which can avoid device underutilization, load imbalance across devices, and frequent kernel launches, interdevice data transfers and inter-device synchronizations by dynamically adjusting the chunk size according to the performance change during runtime. A series of experiments are conducted with 8 representative parallel applications on a hybrid CPU-GPU-MIC system, the results show that the proposed two interdevice task scheduling schemes can achieve the efficient CPU-GPU-MIC co-processing of different parallel applications by effectively partitioning work across devices.INDEX TERMS Data-parallel kernels, heterogeneous systems, many-core accelerators, multi-core CPUs, multi-device co-processing, parallel applications, task scheduling.

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