.png){kind=icon}
返回
Interference-aware execution framework with Co-scheML on GPU clusters
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-021-03299-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, improving the overall resource utilization through efficient scheduling of applications on graphic processing unit (GPU) clusters has been a concern. Traditional cluster-orchestration platforms providing GPUs exclusively for applications constrain high resource utilization. Co-execution of GPU applications is suggested to utilize limited resources. However, the co-execution of GPU applications without considering their diverse characteristics can lead to their unpredictable performances owing to interference resulting from contention and unbalanced usage of resources among applications. This paper proposes an interference-aware execution framework with Co-scheML for various GPU applications such as high performance computing (HPC), deep learning (DL) training, and DL inference. Various resource-usage characteristics of GPU applications are analyzed and profiled to identify various degrees of their application interference. As interference prediction is challenging owing to the complexity of GPU systems, an interference model is generated by applying defined GPU metrics to machine learning (ML) models. A Co-scheML scheduler deploys applications to minimize the interference using the predicted interference from the constructed model. Experimental results of our framework demonstrated that the resource utilization improved by 24%, the average job completion time (JCT) improved by 23%, and the makespan shortened by 22% on average, compared to baseline schedulers.
Keyword:
GPU applications
Interference
Co-execution
Co-ScheML scheduler
Resource contention
GPU utilization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
4.1
论文数:
5.0K
被引数:
7.5K
机构
暂无机构信息
引用论文
分享收藏Increasing self‐efficacy and knowledge in carer training: Hispanic versus Caucasian.png)
Nursing Open
OAAI
Nursing OpenIF0
OAAI分享收藏Deep Learning Research and Development Platform: Characterizing and Scheduling with QoS Guarantees on GPU Clusters深度学习研发平台: GPU集群上QoS保障的表征和调度
PREAI
分享收藏
查看更多内容