Adaptive Simultaneous Multi-tenancy for GPUs

Bashizade, Ramin; Li, Yuxuan; Lebeck, Alvin R.

doi:10.1007/978-3-030-10632-4_5

Cited by 1 publication

(1 citation statement)

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“…Additionally, a number of strategies have been created to optimize the GPU scheduling strategy. For example, Bashizade et al [26] proposed adaptive simultaneous multi-tenancy allows for sharing the GPU among multiple kernels, as opposed to single kernel multi-tenancy and static simultaneous multi-tenancy. Zhao et al [27] proposed a static kernel fusion and scheduling approach to improve GPU utilization of both types of cores while ensuring the QoS of co-located tasks.…”

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%