Gemini: Enabling Multi-Tenant GPU Sharing Based on Kernel Burst Estimation

Chen, Hung-Hsin; Lin, En-Te; Chou, Yu-Min; Chou, Jerry

doi:10.1109/tcc.2021.3119205

Cited by 2 publications

(1 citation statement)

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“…Dogan et al [29] analyzed the task execution reliability on heterogeneous computing systems, and combined this into applications, dynamic level to implement the scheduling algorithm. Chen et al [30] designed and implemented Gemini, a user-space runtime scheduling framework to enable fine-grained GPU allocation control with support for multi-tenancy and elastic allocation, which are critical for cloud and resource providers These research findings have been established based on the superiority of scheduling algorithms to improve the utilization of GPU.…”

Section: Related Workmentioning

confidence: 99%

Workload Analysis and Prediction of Multi-type GPU in Heterogeneous GPU Clusters

Wang

Chen

Shi

2022

Preprint

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Heterogeneous GPU clusters play an important role in processing parallel applications and massive data sets in the cloud platform. However, due to the diversity of GPU types, how to effectively allocate various GPU types is a challenge. This paper first analyzes the characteristics of request and allocation for various GPU types based on Alibaba cluster data. Then we propose a method to adaptively select the best model to predict demand of various GPU types, and feature extraction from the best model. Further, we design a model based on Long Short Term Memory (LSTM) to forecast allocation of each GPU type. Finally, the extensive experimental results demonstrate the promising performance compared with multiple baseline methods by using real-world trace data from Alibaba cloud data centers. The trial illustrates that the demand prediction accuracy of the adaptive selection method reaches 87%, while the proposed prediction allocation model yields better performances with root mean square error and mean absolute error of 1.78 and 0.85, respectively.

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Section: Related Workmentioning

confidence: 99%