@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00222956,
 author = {Taiki, Miyakawa and Yanzhi, Li and Midori, Sugaya and Taiki, Miyakawa and Yanzhi, Li and Midori, Sugaya},
 book = {Proceedings of Asia Pacific Conference on Robot IoT System Development and Platform},
 month = {Dec},
 note = {A Multi-node Edge Computing (MEC) system that integrates hardware with different accelerators, such as GPUs and FPGAs, has been proposed for high-performance computing applications with low power consumption. Generally, a GPU node that connects to the MEC system provides a huge computation resource for AI applications. However, the current implementation of the MEC system executes jobs serially, which causes GPU utilization to decrease when processing multiple jobs. Moreover, there is a lack of a mutual-execution mechanism that avoids resource competition on the GPU, which causes abnormal program termination and may result in incorrect processing. To improve the efficiency and reliable execution of the multiple jobs on the GPU node, we propose a job pipeline mechanism that receives multiple jobs from the requester and executes them as node programs parallelly cooperate with the CPU. Also, we propose a mechanism that provides mutual exclusion to avoid abnormal termination of the jobs. In the evaluation, we found the efficient use of the GPUs of the system and avoid abnormal termination despite the multiple job assignment., A Multi-node Edge Computing (MEC) system that integrates hardware with different accelerators, such as GPUs and FPGAs, has been proposed for high-performance computing applications with low power consumption. Generally, a GPU node that connects to the MEC system provides a huge computation resource for AI applications. However, the current implementation of the MEC system executes jobs serially, which causes GPU utilization to decrease when processing multiple jobs. Moreover, there is a lack of a mutual-execution mechanism that avoids resource competition on the GPU, which causes abnormal program termination and may result in incorrect processing. To improve the efficiency and reliable execution of the multiple jobs on the GPU node, we propose a job pipeline mechanism that receives multiple jobs from the requester and executes them as node programs parallelly cooperate with the CPU. Also, we propose a mechanism that provides mutual exclusion to avoid abnormal termination of the jobs. In the evaluation, we found the efficient use of the GPUs of the system and avoid abnormal termination despite the multiple job assignment.},
 pages = {56--59},
 publisher = {情報処理学会},
 title = {Job Pipeline Mechanism of GPU Node for Multi-node Edge Computing (MEC) Systems},
 volume = {2022},
 year = {2022}
}