Exploiting FIFO Scheduler to Improve Parallel Garbage Collection Performance

Qian, Junjie; Srisa-an, Witawas; Seth, Sharad; Jiang, Hong; Li, Du; Peng, Yi

doi:10.1145/3007611.2892248

Cited by 3 publications

(2 citation statements)

References 15 publications

(9 reference statements)

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“…In order to achieve maximum data reuse, the group of windows should be moved to the long side of the group of windows, that is, to the right. When it moves to the right edge of the feature map, the group of windows is moved vertically by p steps, and restart the leftmost image starts to run [32]. Assuming that the feature map height is h, the optimal efficiency can be achieved when & is an integer; the second allocation p method is shown in Figure 3 (b).…”

Section: Figure 2 Parallel Implementation Structure Of Convolution Cmentioning

confidence: 99%

Retracted: Computer Medical Image Segmentation Based on Neural Network

Wang

2020

IEEE Access

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Image segmentation in medical imaging has long been a problem in radiological image processing. Most of the image segmentation methods in traditional vision algorithms are difficult to achieve high-resolution image segmentation due to the complexity of the algorithm. This paper proposes an image segmentation method based on an optimized cellular neural network. This method introduces a non-linear template and data quantization on the basis of a basic network model, which greatly reduces the computational complexity while maintaining the accuracy of image segmentation. We then applied the method to a computer-aided system to classify tumor lesions in mammograms. Finally, we propose an FPGA-based multilevel optimization architecture for energy-efficient cellular neural networks. The optimization scheme includes three levels: system level, module level, and design space. This solution improves computing performance by increasing system parallelism, using data reuse technology to fully utilize loading bandwidth, and using data quantization to reduce computational redundancy. It also introduces pipeline and dual cache structures to optimize memory access, and analyzes the limited resources through the Roofline model. System for best performance. The experimental results show that the FPGA accelerator in this paper can improve unit performance by 34% compared with other existing research work. The nonlinear quantified cellular neural network proposed in this paper can reduce LUT resource consumption by 74% and energy of 48.2%. Compared with the original network, in the two projection position segmentation results of the mammogram, only 1.5% and 0.6% of the accuracy loss, respectively.

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Section: Figure 2 Parallel Implementation Structure Of Convolution Cmentioning

confidence: 99%

Retracted: Computer Medical Image Segmentation Based on Neural Network

Wang

2020

IEEE Access

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“…The user creates a script file, including an executable file and input parameters needed for the execution of his/her application program in a certain format based on the job scheduler and submits it to the batch job scheduler. The batch job scheduler matches the user-requested resources and available resources and allocates available resources sequentially according to a certain service policy, involving first in first out (FIFO) [3], fairness-ware [4], or energy-efficient [5]. Thereafter, the user job is executed using the allocated computing resources.…”

Section: Introductionmentioning

confidence: 99%

Queue congestion prediction for large-scale high performance computing systems using a hidden Markov model

2022

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To share limited, large-capacity resources, the high-performance computing field provides services by allocating available resources to jobs through batch job schedulers. Therefore, it is natural that a queue waiting time occurs until the resources are available if resources are not sufficient. The prediction of queue waiting time is very useful to improve overall resource utilization. However, the queue waiting time is very difficult to predict because it is significantly affected by the many factors such as applied scheduling algorithm and characteristics of the executed job. In this study, a method of predicting queue waiting time using only the historical log data created by the batch job scheduler is examined. Specifically, a method of predicting queue waiting time based on a hidden Markov model is proposed. It has the following three stages. First, outliers are removed by applying the outlier detection algorithm using a statistics-based parametric method. Second, the parameters of the hidden state are estimated using the observed queue waiting time sequence based on the historical job log. Third, the queue waiting interval at time $$t+1$$ t + 1 is provided using the estimated parameters at time t. Comparing the prediction accuracy with those of the other prediction methods, experimental results show that the proposed algorithm improves the prediction accuracy by up to 60%.

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