Elastic Multi-resource Fairness: Balancing Fairness and Efficiency in Coupled CPU-GPU Architectures

Tang, Shanjiang; He, Bingsheng; Zhang, Shuhao; Niu, Zhaojie

doi:10.1109/sc.2016.74

Cited by 13 publications

(5 citation statements)

References 22 publications

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“…Reference [27] proposed an AlloX strategy for achieving efficient prediction of access-side machine learning business resources, thereby enabling rational utilization of GPU and CPU resources and reducing the cost of CPU/GPU data centers. The authors of [28] study an elastic multi-resource allocation strategy based on a coupled CPU-GPU shelf, which provides resource availability while better ensuring user fairness. In [29], a deep Q-learning resource prediction and scheduling algorithm for GPU is proposed, which designed three prototypes of resource management systems, the simulation results show significant improvements in resource utilization compared to ordinary heuristics.…”

Section: A Related Workmentioning

confidence: 99%

“…The heterogeneous computing resource scheduling has been studied in [22], [23], [24], and [25], but it mainly considers the static resource scheduling scenario, the temporary resource switching scenario is ignored. Meanwhile, although resource prediction has effectively improved the flexible scheduling ability of computing resources in [26], [27], [28], [29], and [30], but not consider actual costs and profit from the perspective of operators and how to maximize the benefits of computing operations. Besides, the current research on resource scheduling based on game theory is mainly oriented to cloud computing power pricing, network elements, and other fields [31], [32], [33], and [34].…”

Section: B Motivation and Contributionsmentioning

confidence: 99%

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Evolutionary Game Theory-Based Optimal Scheduling Strategy for Heterogeneous Computing

She

Zhao

2023

IEEE Access

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With the development of intelligent applications, simply relying on traditional single type of computing unit cannot efficiently satisfy diverse cloud requirements. The emergence of heterogeneous computing can efficiently achieve the adaptation of these intelligent applications by using different types of processing units such as Graphics Processing Unit (GPU) and Field Programmable Gate Array (FPGA). However, the trade-off between profit and costs in the process of scheduling heterogeneous computing resources is also an issue worthy of attention. To address this challenge, this work establishes a heterogeneous computing resource scheduling model based on Stackelberg differential game, which includes three roles Computing Power Trading Platforms (CPTPs), Heterogeneous Computing Service Providers (HCSPs), and Heterogeneous Computing Application Providers (HCAPs). The objective is to maximize utility function of CPTPs and HCSPs subject to rental ratio, pricing strategy and energy consumption of resource scheduling, which has proved that there exists a Stackelberg Nash Equilibrium (NE) solution. The Support Vector Machine based on Artificial Fish (SVM-AF) is proposed to predict the access times of heterogeneous computing applications. In addition, the distributed iteration method and Cauchy distribution is adopted to optimize the computing price strategy and improve its convergence performance. The simulation results show that compared with other strategies, the proposed strategy can effectively improve computing revenue of user experience and while reducing energy consumption in the process of resource scheduling.

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Section: A Related Workmentioning

confidence: 99%

Section: B Motivation and Contributionsmentioning

confidence: 99%

Evolutionary Game Theory-Based Optimal Scheduling Strategy for Heterogeneous Computing

She

Zhao

2023

IEEE Access

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“…Barik et al (2014) tried to map irregular C++ applications to the GPU device on heterogeneous processors. Fairness and efficiency are two major concerns for shared system users; Tang et al (2016) introduced multi-resource fairness and efficiency on heterogeneous processors. Zhang et al (2017a) considered the irregularity inside workload and architecture differences between CPUs and GPUs, and then proposed a method that can distribute the relatively regular part of workload to GPUs while remain irregular part to CPUs on integrated architectures.…”

Section: Accelerating Irregular Applications On Heterogeneous Processorsmentioning

confidence: 99%

Performance evaluation and analysis of sparse matrix and graph kernels on heterogeneous processors

et al. 2019

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Heterogeneous processors integrate very distinct compute resources such as CPUs and GPUs into the same chip, thus can exploit the advantages and avoid disadvantages of those compute units. We in this work evaluate and analyze eight sparse matrix and graph kernels on an AMD CPU-GPU heterogeneous processor by using 956 sparse matrices. Five characteristics, i.e., load balancing, indirect addressing, memory reallocation, atomic operations, and dynamic characteristics are our major considerations. The experimental results show that although the CPU and GPU parts access the same DRAM, very different performance behaviors are observed. For example, though the GPU part in general outperforms the CPU part, it cannot achieve the best performance in all cases given by the CPU part. Moreover, the bandwidth utilization of atomic operations on heterogeneous processors can be much higher than a high-end discrete GPU.

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“…Some recent work has addressed coupled environments where the cores and the co-processor units are integrated on a single chip [11,18,20,21]. For instance, in [18], to show the effects of using an integrated GPU, the authors propose specialized scan and aggregation operations.…”

Section: Related Workmentioning

confidence: 99%

A morsel-driven query execution engine for heterogeneous multi-cores

et al. 2019

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Currently, we face the next major shift in processor designs that arose from the physical limitations known as the "dark silicon effect". Due to thermal limitations and shrinking transistor sizes, multi-core scaling is coming to an end. A major new direction that hardware vendors are currently investigating involves specialized and energy-efficient hardware accelerators (e.g., ASICs) placed on the same die as the normal CPU cores. In this paper, we present a novel query processing engine called SiliconDB that targets such heterogeneous processor environments. We leverage the Sparc M7 platform to develop and test our ideas. Based on the SSB benchmarks, as well as other micro benchmarks, we compare the efficiency of SiliconDB with existing execution strategies that make use of co-processors (e.g., FPGAs, GPUs) and demonstrate speed-up improvements of up to 2×.

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Elastic Multi-resource Fairness: Balancing Fairness and Efficiency in Coupled CPU-GPU Architectures

Cited by 13 publications

References 22 publications

Evolutionary Game Theory-Based Optimal Scheduling Strategy for Heterogeneous Computing

Evolutionary Game Theory-Based Optimal Scheduling Strategy for Heterogeneous Computing

Performance evaluation and analysis of sparse matrix and graph kernels on heterogeneous processors

A morsel-driven query execution engine for heterogeneous multi-cores

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