.png){kind=icon}
.png){kind=icon}
Return
Efficient Inter-Device Task Scheduling Schemes for Multi-Device Co-Processing of Data-Parallel Kernels on Heterogeneous Systems
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3073955.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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, inter-device 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 inter-device task scheduling schemes can achieve the efficient CPU-GPU-MIC co-processing of different parallel applications by effectively partitioning work across devices.
Keywords:
Dynamic scheduling
Task analysis
Processor scheduling
Kernel
Scheduling
Performance evaluation
Graphics processing units
Data-parallel kernels
heterogeneous systems
many-core accelerators
multi-core CPUs
multi-device co-processing
parallel applications
task scheduling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.