CPU–GPU Utilization Aware Energy-Efficient Scheduling Algorithm on Heterogeneous Computing Systems

Tang, Xiaoyong; Fu, Zhuojun

doi:10.1109/access.2020.2982956

Cited by 24 publications

(7 citation statements)

References 37 publications

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“…Nowadays, more and more artificial intelligence products are listed and integrated into people's life. In the near future, artificial intelligence technology will have a significant impact on human society and human production and life [1,2]. Seeing the rapid development of information industry and Internet in the past, most of the credit depends on the rapid development of integrated circuits.…”

Section: Introductionmentioning

confidence: 99%

The Application of AI Technology in GPU Scheduling Algorithm Optimization

Yan

Liu

Shao

2022

Wireless Communications and Mobile Computing

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With the rapid development of integrated circuit technology, GPU computing capabilities continue to improve. Due to the continuous improvement and improvement of GPU programming capabilities, functions, and performance, GPUs have been widely used in the field of high-tech general-purpose computers. This article is aimed at studying the optimization of GPU scheduling algorithm based on AI technology. Through a combination of theoretical analysis and simulation experiments, the concepts of artificial intelligence technology and GPU scheduling are explained, and the impact of GPU architecture and GPGPU load on the energy efficiency of GPGPU is explained. On the basis of comprehensive analysis of GPU cluster characteristics, a new GA-TP scheduling algorithm based on genetic algorithm was designed, and based on the energy efficiency of the cluster, a simulation verification platform was built for the accuracy of simulation. Experimental results show that the acceleration rate of the GA-TP algorithm is significantly lower than that of the HEFT algorithm, the average acceleration rate is reduced by nearly 25%, and the scheduling efficiency of the GA-TP algorithm is higher.

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Section: Introductionmentioning

confidence: 99%

The Application of AI Technology in GPU Scheduling Algorithm Optimization

Yan

Liu

Shao

2022

Wireless Communications and Mobile Computing

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“…The aim is to configure the appropriate GPU voltage/frequency for each task through mathematical optimization. The same problem is tackled in [14] over a node system with CPU-GPU architecture. This approach implements a heuristic-based job scheduling method and a hybrid scheme of particle swarm optimization.…”

Section: A Cpu-gpu Based Systemsmentioning

confidence: 99%

Sustainable Artificial Intelligence Systems: An Energy Efficiency Approach

Hidalgo,

Fenández-de_Vega,

Ceberio

et al. 2023

Preprint

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<p>The energy consumption of Artificial Intelligence (AI) systems has increased 300,000-fold from 2012 to now, and data centers running massive AI software produce up to 5-9% of global electricity demand and 2% of all CO2 emissions. Such an increase in energy consumption has been partially motivated by the strong development of new AI-specific architectures to improve the performance of AI models. Nevertheless, the AI community has recently become aware of the importance of considering energy efficiency as a metric when developing AI techniques. To date, a great effort has been made to find optimal AI model configurations that provide the best solution in the shortest possible time. However, only a few works have sought a compromise between energy cost and system performance. This paper analyses recent efforts in these directions and proposes the path toward energy-efficient AI. We describe a set of energy efficiency strategies for applying and deploying AI models on different computing infrastructures in search of democratizing an environmentally sustainable AI. To that end, we propose a full-stack approach of energy-efficient AI and analyze the role that different types of users should play, tackling the energy-focused optimization in all steps of the AI model design flow, from the high levels of models and algorithm design to the low levels ones, more related to the hardware and architecture.<br> </p>

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“…For the same source node and destination node, multiple computing tasks can be aggregated into a single wavelength channel for data transmission to improve network resource utilization. The specific parameters are shown in Table 1, where 𝑉 is the set of computing nodes with CPU and GPU, 𝑉 is the computing nodes with CPU, 𝐶 and 𝐶 are CPU and GPU capacity of computing node, respectively [28], 𝑁 is the number of wavelength channels [29], b is the bandwidth capacity of each wavelength [3], and 𝜐 is The specific parameters are shown in Table 1, where V h is the set of computing nodes with CPU and GPU, V c is the computing nodes with CPU, C CPU and C GPU are CPU and GPU capacity of computing node, respectively [28], N W is the number of wavelength channels [29], b is the bandwidth capacity of each wavelength [3], and υ is the transmission speed [30]. In addition, the proportion of general, CPU-intensive, and GPU-intensive applications is r g , r C , and r G , respectively.…”

Section: Simulation Setupmentioning

confidence: 99%

Application-Aware Resource Allocation Based on Benefit–Cost Ratio in Computing Power Network with Heterogeneous Computing Resources

Wang,

Li,

Guo

et al. 2023

Photonics

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The computing power network (CPN) is expected to realize the efficient provisioning of heterogeneous computing power through the collaboration between cloud computing and edge computing. Heterogeneous computing resources consist of CPU, GPU, and other types of computing power. Different types of applications may have diverse requirements for heterogeneous computing resources, such as general applications, CPU-intensive applications, and GPU-intensive applications. Service providers are concerned about how to dynamically provide heterogeneous computing resources for different applications in a cost-effective manner, and how to deploy more applications as much as possible with limited resources. In this paper, the concept of the benefit–cost ratio (BCR) is proposed to quantify the usage efficiency of CPU and GPU in CPNs. An application-aware resource allocation (AARA) algorithm is designed for processing different types of applications. With massive simulations, we compare the performance of the AARA algorithm with a benchmark. In terms of blocking probability, resource utilization, and BCR, AARA achieves better performance than the benchmark. The simulation results indicate that more computing tasks can be accommodated by reducing 3.7% blocking probability through BCR-based resource allocation.

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CPU–GPU Utilization Aware Energy-Efficient Scheduling Algorithm on Heterogeneous Computing Systems

Cited by 24 publications

References 37 publications

The Application of AI Technology in GPU Scheduling Algorithm Optimization

The Application of AI Technology in GPU Scheduling Algorithm Optimization

Sustainable Artificial Intelligence Systems: An Energy Efficiency Approach

Application-Aware Resource Allocation Based on Benefit–Cost Ratio in Computing Power Network with Heterogeneous Computing Resources

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