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Real-time frame rate controlled dynamic voltage and frequency scaling for GPU power consumption optimization

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AIP Conference Proceedings 2024年第1期3131卷

作者： Guruprasad Konnurmath Satyadhyan Chickerur School of Computer Science and Engineering K L E Technological University Hubballi 580031 India Centre for High Performance Computing K L E Technological University Hubballi 580031 India

Technological advancements in the domain of computer graphics have enhanced the visual realism in video games, movies, and various forms of media. This comprises of realistic visual presentation, best rendering mechanisms, improved textures, naturalistic light effects etc. This paper focus on frame rate monitoring integrated with dynamic voltage and frequency scaling (DVFS) technique to reduce power consumption on rendering pipeline mainly the vertex and pixel shaders during 3D scene render process. To understand shader power analysis, power consumption on GPU is investigated initially at runtime to set rendering configuration by limiting framerates of rendered scene and generates dynamic power-aware rendering framework by analyzing the workload on frames. In the implementation, optimized power on GPU is attained with the combination of three major mechanisms: the TensorFlow tools and libraries, the Graphics Rendering Parameter Control mechanism and the Dynamic Voltage and Frequency Control mechanism. Initially the range of framerates are set in-between 30 fps and 60 fps to consider the state as graphics rendering parameter control mechanism. Based on workload of each frame, if current frame rate is greater than range of 60 fps then GPU frequency is reduced and if lesser than 30 fps then frequency is increased using DVFS mechanism. So that optimal power settings are achieved, avoiding unnecessary power wastage on GPU. Parallelly TensorFlow libraries boost computational speed improving time and memory frequencies. Results show efficient render of 3D scene with power savings around 35% and decreased computational complexity. The results obtained through the proposed methodology forms the basis for performance and power consumption analysis of various other stages of graphics rendering pipeline. Minimal power consumption per frame-rate is achieved resulting into the best performance-power oriented results.

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Natural Language Video Localization: A revisit in span-based question answering framework

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arXiv 2021年

作者： Zhang, Hao Sun, Aixin Jing, Wei Zhen, Liangli Zhou, Joey Tianyi Goh, Rick Siow Mong The Institute of High Performance Computing A*STAR Singapore138632 Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Institute of Infocomm Research A*STAR Singapore138632 Singapore

—Natural Language Video Localization (NLVL) aims to locate a target moment from an untrimmed video that semantically corresponds to a text query. Existing approaches mainly solve the NLVL problem from the perspective of computer vision by formulating it as ranking, anchor, or regression tasks. These methods suffer from large performance degradation when localizing on long videos. In this work, we address the NLVL from a new perspective, i.e., span-based question answering (QA), by treating the input video as a text passage. We propose a video span localizing network (VSLNet), on top of the standard span-based QA framework (named VSLBase), to address NLVL. VSLNet tackles the differences between NLVL and span-based QA through a simple yet effective query-guided highlighting (QGH) strategy. QGH guides VSLNet to search for the matching video span within a highlighted region. To address the performance degradation on long videos, we further extend VSLNet to VSLNet-L by applying a multi-scale split-and-concatenation strategy. VSLNet-L first splits the untrimmed video into short clip segments;then, it predicts which clip segment contains the target moment and suppresses the importance of other segments. Finally, the clip segments are concatenated, with different confidences, to locate the target moment accurately. Extensive experiments on three benchmark datasets show that the proposed VSLNet and VSLNet-L outperform the state-of-the-art methods;VSLNet-L addresses the issue of performance degradation on long videos. Our study suggests that the span-based QA framework is an effective strategy to solve the NLVL problem. Copyright © 2021, The Authors. All rights reserved.

关键词： Benchmarking

Software engineering for IoT-driven data analytics applications

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arXiv 2021年

作者： Ahmad, Aakash Fahmideh, Mahdi Altamimi, Ahmed B. Katib, Iyad Albeshri, Aiiad Alreshidi, Abdulrahman Alanazi, Adwan Mehmood, Rashid Abbasi, Ahmad College of Computer Science and Engineering University of Ha'il Saudi Arabia School of Computing and Information Technology University of Wollongong Australia Department of Computer Science FCIT King Abdul Aziz University Jeddah Saudi Arabia High Performance Computing Center King Abdulaziz University Jeddah Saudi Arabia

Internet of Things Driven Data Analytics (IoT-DA) has the potential to excel data-driven operationalisation of smart environments. However, limited research exists on how IoT-DA applications are designed, implemented, operationalised, and evolved in the context of software and system engineering life-cycle. This article empirically derives a framework that could be used to systematically investigate the role of software engineering (SE) processes and their underlying practices to engineer IoT-DA applications. First, using existing frameworks and taxonomies, we develop an evaluation framework to evaluate software processes, methods, and other artefacts of SE for IoT-DA. Secondly, we perform a systematic mapping study to qualitatively select 16 processes (from academic research and industrial solutions) of SE for IoT-DA. Thirdly, we apply our developed evaluation framework based on 17 distinct criterion (a.k.a. process activities) for fine-grained investigation of each of the 16 SE processes. Fourthly, we apply our proposed framework on a case study to demonstrate development of an IoT-DA healthcare application. Finally, we highlight key challenges, recommended practices, and the lessons learnt based on framework's support for process-centric software engineering of IoT-DA. The results of this research can facilitate researchers and practitioners to engineer emerging and next-generation of IoT-DA software applications. © 2021, CC BY.

关键词： Data Analytics

Hsm2: A hybrid and scalable metadata management method in distributed file systems 10th

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Hsm2: A hybrid and scalable metadata management method in di...

10th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2019

作者： Wang, Yiduo Chen, Youxu Shao, Xinyang Chen, Jinzhong Yuan, Liu Xu, Yinlong School of Computer Science and Technology University of Science and Technology of China AnHui Province Key Laboratory of High Performance Computing Hefei China East China Research Institute of Electronic Engineering Hefei China China Academy of Electronics and Information Technology Beijing China

In the bigdata era, metadata performance is critical in modern distributed file systems. Traditionally, the metadata management strategies like the subtree partitioning method focus on keeping namespace locality, while the other ones like the hash-based mapping method aim to offer good load balance. Nevertheless, none of these methods achieve the two desirable properties simultaneously. To close this gap, in this paper, we propose a novel metadata management scheme, HSM2, which combines the subtree partitioning and hash-based mapping method together. We implemented HSM2 in CephFS, a widely deployed distributed file systems, and conducted a comprehensive set of metadata-intensive experiments. Experimental results show that HSM2 can achieve better namespace locality and load balance simultaneously. Compared with CephFS, HSM2 can reduce the completion time by 70% and achieve 3.9× overall throughput speedup for a file-scanning workload. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Metadata

Blockchain oracle design patterns

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arXiv 2021年

作者： Pasdar, Amirmohammad Dong, Zhongli Lee, Young Choon Aglive Lab 8 Hadenfeld Ave Macquarie ParkNSW2113 Australia Department of Computing Macquarie University SydneyNSW2109 Australia Center for Distributed and High Performance Computing School of Computer Science The University of Sydney NSW2006 Australia

Blockchain is a form of distributed ledger technology (DLT) where data is shared among users connected over the internet. Transactions are data state changes on the blockchain that are permanently recorded in a secure and transparent way without the need of a third party. Besides, the introduction of smart contracts to the blockchain has added programmability to the blockchain and revolutionized the software ecosystem leading toward decentralized applications (DApps) attracting businesses and organizations to employ this technology. Although promising, blockchains and smart contracts have no access to the external systems (i.e., off-chain) where real-world data and events resides;consequently, the usability of smart contracts in terms of performance and programmability would be limited to the on-chain data. Hence, blockchain oracles are introduced to mitigate the issue and are defined as trusted third-party services that send and verify the external information (i.e., feedback) and submit it to smart contracts for triggering state changes in the blockchain. In this paper, we will study and analyze blockchain oracles with regard to how they provide feedback to the blockchain and smart contracts. We classify the blockchain oracle techniques into two major groups such as voting-based strategies and reputation-based ones. The former mainly relies on participants' stakes for outcome finalization while the latter considers reputation in conjunction with authenticity proof mechanisms for data correctness and integrity. We then provide a structured description of patterns in detail for each classification and discuss research directions in the end. © 2021, CC BY.

关键词： Smart contract

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 IEEE.

关键词： Image enhancement

GDPNet: Refining latent multi-view graph for relation extraction

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arXiv 2020年

作者： Xue, Fuzhao Sun, Aixin Zhang, Hao Chng, Eng Siong School of Computer Science and Engineering Nanyang Technological University Singapore Institute of High Performance Computing A*STAR Singapore

Relation Extraction (RE) is to predict the relation type of two entities that are mentioned in a piece of text, e.g., a sentence or a dialogue. When the given text is long, it is challenging to identify indicative words for the relation prediction. Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships among the tokens in the sequence. In this paper, we propose to construct a latent multi-view graph to capture various possible relationships among tokens. We then refine this graph to select important words for relation prediction. Finally, the representation of the refined graph and the BERT-based sequence representation are concatenated for relation extraction. Specifically, in our proposed GDPNet (Gaussian Dynamic Time Warping Pooling Net), we utilize Gaussian Graph Generator (GGG) to generate edges of the multi-view graph. The graph is then refined by Dynamic Time Warping Pooling (DTWPool). On DialogRE and TACRED, we show that GDPNet achieves the best performance on dialogue-level RE, and comparable performance with the state-of-the-arts on sentence-level RE. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation

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arXiv 2022年

作者： Zhang, Wenju Zhang, Xiang Liao, Qing Lan, Long Wang, Mengzhu Wang, Wei Peng, Baoyun Ding, Zhengming Institute for Quantum Information State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian China Tulane University New OrleansLA United States

Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme. In this paper, we identify a new property termed equity, which indicates the balance degree of predicted classes, to demystify the efficacy of nuclear norm maximization for UDA theoretically. With this in mind, we offer a new discriminability-and-equity maximization paradigm built on squares loss, such that predictions are equalized explicitly. To verify its feasibility and flexibility, two new losses termed Class Weighted Squares Maximization (CWSM) and Normalized Squares Maximization (NSM), are proposed to maximize both predictive discriminability and equity, from the class level and the sample level, respectively. Importantly, we theoretically relate these two novel losses (i.e., CWSM and NSM) to the equity maximization under mild conditions, and empirically suggest the importance of the predictive equity in UDA. Moreover, it is very efficient to realize the equity constraints in both losses. Experiments of cross-domain image classification on three popular benchmark datasets show that both CWSM and NSM contribute to outperforming the corresponding counterparts. © 2022, CC BY.

关键词： Image classification

A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

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A Novel Deep Learning Approach Featuring Graph-Based Algorit...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Keliang Zhao Jovial Niyogisubizo Linxia Xiao Yi Pan Dongqing Wei Didi Rosiyadi Yanjie Wei Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China Research Center for Artificial Intelligence and Cybersecurity National Research and Innovation Agency Bandung Indonesia

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs.

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