A Study on the Performance of Oracle Grid Engine for Computing Intensive Applications

Koliçi, Vladi; Herrero, Albert; Xhafa, Fatos; Barolli, Leonard

doi:10.1109/incos.2014.39

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Traditionally, based on the different utilization of resources, jobs can be generally classified into three categories: I/O intensive, memory intensive, and compute-intensive [20]. By far, a majority of previous studies treat CPU as the main resource consumed by compute-intensive jobs [21][22][23]. But, as the emergence of deep learning, the job type can be specifically divided into CPU-intensive and GPU-intensive.…”

Section: Introductionmentioning

confidence: 99%

An Improved ANN-based Power Prediction Algorithm for Multi-type Jobs on Heterogeneous Computing Server

Chao

2022

Preprint

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This paper exploits the advantage of Artificial Neural Network (ANN) to design and implement an improved algorithm of power prediction on heterogeneous computing server with various types of jobs.First, the impact of multi-type of jobs has been taken into account. We divide the jobs into four categories: CPU-intensive, memory-intensive, I/O-intensive and GPU-intensive. We collect the data trace from these jobs by a set of benchmarks.Then, based on the collected data trace, power prediction algorithm has been established, so that developing an improved ANN-based power prediction algorithm with CNN-BiLSTM neural network and attention mechanism.Last, the experiment has been conducted via real-world heterogeneous computing based platform.Compared with the traditional neural network based prediction algorithms and non-neural network based prediction algorithms, our proposed prediction algorithm has better performance in prediction accuracy on power consumption.

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Section: Introductionmentioning

confidence: 99%

An Improved ANN-based Power Prediction Algorithm for Multi-type Jobs on Heterogeneous Computing Server

Chao

2022

Preprint

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CBLA_PM: an improved ann-based power consumption prediction algorithm for multi-type jobs on heterogeneous computing server

Chao

2023

Cluster Comput

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A Study on the Performance of Oracle Grid Engine for Computing Intensive Applications

Cited by 2 publications

References 14 publications

An Improved ANN-based Power Prediction Algorithm for Multi-type Jobs on Heterogeneous Computing Server

An Improved ANN-based Power Prediction Algorithm for Multi-type Jobs on Heterogeneous Computing Server

CBLA_PM: an improved ann-based power consumption prediction algorithm for multi-type jobs on heterogeneous computing server

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