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An Efficient Vectorization Scheme for Stencil Computation

An Efficient Vectorization Scheme for Stencil Computation

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international symposium on Parallel and distributed Processing (IPDPS)

作者： Kun Li Liang Yuan Yunquan Zhang Yue Yue Hang Cao State Key Laboratory of Computer Architecture Chinese Academy of Sciences Institute of Computing Technology Beijing School of Computer Science and Technology University of Chinese Academy of Sciences Beijing

Stencil computation is one of the most important kernels in various scientific and engineering applications. A variety of work has focused on vectorization and tiling techniques, aiming at exploiting the in-core data parallelism and data locality respectively. In this paper, the downsides of existing vectorization schemes are analyzed. Briefly, they either incur data alignment conflicts or hurt the data locality when integrated with tiling. Then we propose a novel transpose layout to preserve the data locality for tiling and reduce the data reorganization overhead for vectorization simultaneously. To further improve the data reuse at the register level, a time loop unroll-and-jam strategy is designed to perform multistep stencil computation along the time dimension. Experimental results on the AVX2 and AVX-S12 CPUs show that our approach obtains a competitive performance with the classic vectorization methods (Auto Vectorization and Data Reorganization), state-of-the-art compilers (Pluto and SDSL), and highly-optimized work (DLT and Tessellation).

关键词： Concurrent computing distributed processing Layout Parallel processing Registers Computational efficiency Kernel

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Communication and Energy Efficient Edge Intelligence

Communication and Energy Efficient Edge Intelligence

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IEEE/ACM international symposium on Big Data computing (BDC)

作者： Sabtain Ahmad Institute of Information Systems Engineering Vienna University of Technology Vienna Austria

Driven by the rapid advances in Artificial Intelligence of Things (AIoT), billions of mobile and IoT devices are connected to the internet, generating huge quantities of data at the network edge. Meanwhile, traditional analytics approaches such as cloud computing and centralized AI are unable to manage these massively distributed heterogeneous data primarily because 1) moving a tremendous amount of data across the network poses severe challenges to network capacity 2) cloud-based analytics can result in prohibitively high transmission delays 3) transporting data containing private information over the network poses serious concerns for the privacy and may not even be possible due to regulations like GDPR. Accelerated by the success of AI and IoT technologies, there is an urgent need to push AI to the network edge to tap the full potential of big data.

关键词： Cloud computing Data privacy distributed databases Big Data Regulation Energy efficiency Delays

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E-commerce Service Equilibrium Prediction Simulation Based on Ontology

E-commerce Service Equilibrium Prediction Simulation Based o...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Wu Zhong Physical Education School Wuhan Business University Wuhan China

ISBN: (纸本)9781665428903

Accurately predicting the needs of users in the process of e-commerce services can expand the scope of e-commerce services and improve the quality of e-commerce services. When forecasting demand, it is necessary to establish different forecasting indicators for different factors that affect e-commerce services. The traditional dual-neighbor selection strategy can only select recommended behaviors based on a single indicator, which reduces the demand for e-commerce services. Accuracy. This paper proposes an ontology-based method for predicting user demand balance in the process of e-commerce services. Firstly, construct an e-commerce service balanced recommendation user ontology, calculate the semantic similarity between the ontology, and obtain a set of similar users. On this basis, predict the neighbors of the target user as the prediction group, and calculate the trust subgroups with higher prediction probability in the group. Through the dynamic measurement of uncertain neighbors for balanced prediction, effectively completed the balanced prediction of user demand in e-commerce services. Simulation experiments prove that the proposed method can effectively improve the accuracy of user demand prediction in e-commerce services, and it is valuable.

关键词： Computational modeling Semantics Measurement uncertainty Ontologies Predictive models Electronic commerce Forecasting

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An Investigation of Block Chains for Attaining Sustainable Society

An Investigation of Block Chains for Attaining Sustainable S...

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Advance computing and Innovative Technologies in engineering (ICACITE), international Conference on

作者： K. K. Ramachandran Fit Lt Renu Lamba Ruchira Rawat Anita Gehlot Adusupalle Muni Raju R. Ponnusamy Management/Commerce/International Business DR G R D College of Science India Department of Management Studies Graphic Era Deemed to be University Dehradun Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Uttaranchal Institute of Technology Uttaranchal University India Department of Marketing Sri Balaji University Pune(SBUP) Department of Computer Science & Engineering Chennai Institute of Technology

It's possible to define blockchain technology as the DLT-based technology upon which cryptocurrencies and other distributed ledgers rely. This innovation extends well beyond Bitcoin and other forms of digital money. With its help, a new Internet era may dawn in which sensitive information can be sent, stored, and managed without fear of tampering or other forms of corruption. Because of its potential to combat corruption and help the impoverished, among other things, this technology is worth investing in. Any kind of transaction, such as the sale of a home or a stock, may be tracked nearly instantly. Sustainable development is at the heart of today's modern world. Technology like this may be useful in the pursuit of sustainability, which involves improving the here and now without jeopardizing the future. To be really useful, though, this innovation requires a robust digital governance structure. The purpose of this article is to investigate Blockchain Technology as a tool for promoting long-term, environmentally friendly growth in India, and to identify contexts where it may be particularly useful. How the Indian economy may adopt this technology and incorporate it into its system to achieve Sustainable Development is the topic of my paper. Capital market, accountable production and consumption, environmental protection, providing legal identity for everybody, property registration, and transparent philanthropy are all possible applications of this technology in India. The research methods used in this paper are entirely inductive, with a focus on secondary sources of information.

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One Case of THOUGHT: Industry-University Converged Education Practice on Open Source 17th

One Case of THOUGHT: Industry-University Converged Education...

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17th international Conference on Computer science and Education, ICCSE 2022

作者： Yu, Zhiyi Li, Chao Hu, Chaochen Chen, Zhen Yao, Yandong Wu, Jiang Wang, Hao Du, Jialun Guo, Min Wang, Haoyu Li, Weimin Li, Xiu Ma, Xiaodong Zhangi, Yisong Li, Shuangshou Information Engineering Beijing University of Posts and Telecommunication Beijing China Department of Computer Science and Technology Tsinghua University Beijing China BNRist Tsinghua University Beijing China iCenter Tsinghua University Beijing China R&D yMatrix Inc. Beijing China R&D ***. Beijing China R&D Co. Ltd. Beijing2861 China

ISBN: (纸本)9789819924486

Nowadays, more and more data are produced in our life. The rapid development of big data and intelligent computing industry has put forward new demands for talents, while colleges and universities with limited industry experiences are striving to promote the talent cultivation. Therefore, the "Innovative Community of Industry-Education Convergence", named THOUGHT (innovaTive Hub for the cO-development of indUstry and hiGHer educaTion) [1], was founded in 2019. THOUGHT aims to improve education cooperation between teaching staff and industry professionals in the related field. The industry-university converged courses are different from technical trainings in enterprises or the speeches of the leaders. In order to cultivate talents, both sides of industry and education cooperate closely in nearly every aspects in course building, e.g., targets, contents, methods, and assessments. And it is necessary to make the courses systematic for learners in terms of knowledge transmission, ability training and value shaping, so as to make them more competent for challenging work in the future of big data and AI. The industry-university converged courses focus on breaking the barrier of the traditional campus. Compared with the emphasis on theories and ideal experiments of traditional campus, it focuses more on practices and applications. Therefore, it needs to rely on a bridge which is convenient for both sides to participate in. Compared with traditional tools of business platforms, open source platforms are better choices. And open source makes it easier for more learners to stand on the giants’ shoulders. This paper shares the experience of a case of THOUGHT: the industry-university converged course "Foundamentals and Application of distributed Data System" jointly built by Tsinghua University and Greenplum open source community. The target, team, organization, structure and contents, practice project, and future work of this course are all introduced in this paper, which may

关键词： Big data

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A General Offloading Approach for Near-DRAM Processing-In-Memory Architectures

A General Offloading Approach for Near-DRAM Processing-In-Me...

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international symposium on Parallel and distributed Processing (IPDPS)

作者： Dan Chen Hai Jin Long Zheng Yu Huang Pengcheng Yao Chuangyi Gui Qinggang Wang Haifeng Liu Haiheng He Xiaofei Liao Ran Zheng National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Clusters and Grid Computing Lab Huazhong University of Science and Technology China

Processing-in-memory (PIM) is promising to solve the well-known data movement challenge by performing in-situ computations near the data. Leveraging PIM features is pretty profitable to boost the energy efficiency of applications. Early studies mainly focus on improving the programmability for computation offloading on PIM architectures. They lack a comprehensive analysis of computation locality and hence fail to accelerate a wide variety of applications. In this paper, we present a general-purpose instruction-level offloading technique for near-DRAM PIM architectures, namely IOTPIM, to exploit PIM features comprehensively. IOTPIM is novel with two technical advances: 1) a new instruction offloading policy that fully considers the locality of the whole on-chip cache hierarchy, and 2) an offloading performance benefit prediction model that directly predicts offloading performance benefits of an instruction based on the input dataset characterizes, preserving low analysis overheads. The evaluation demonstrates that IOTPIM can be applied to accelerate a wide variety of applications, including graph processing, machine learning, and image processing. IOT-PIM outperforms the state-of-the-art PIM offloading techniques by 1.28×-1.51× while ensuring offloading accuracy as high as 91.89% on average.

关键词： Analytical models Technological innovation distributed processing Systematics Image processing Computer architecture Machine learning

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Modification terms to the Black-Scholes model based on the functional volatility model

Modification terms to the Black-Scholes model based on the f...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Xiaozheng Lin Wei Chen Meiqing Wang Choi-Hong Lai College of Mathematics and Computer Science Fuzhou University Fuzhou China University of Greenwich London UK

ISBN: (纸本)9781665428903

The Black-Scholes model (B-S model) is a common used option pricing model, which assumes that the volatility and the interest rate are constants. However, the empirical analysis shows that the option prices obtained from the B-S model are quite different from the market prices. The traditional improve-ment to the B-S model is to replace the volatility constants or interest rate constants with variables. This paper proposes the concept of modification term of Black-Scholes model, which is different from the traditional methods. The errors of the B-S model may be calculated through the market data, and is fitted as a function of a functional implied volatility and the option gamma. The fitted function can be considered as the modification term of the Black-Scholes model. The functional implied volatility can be constructed by using the Gaussian semi-parametric implied volatility model proposed by Ref. [4]. Experimental results show that the modified model has a better predictive effect on option pricing.

关键词： Analytical models Economic indicators Computational modeling Pricing Predictive models Mathematical models Data models

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Improved BiLSTM Model For Online Food Safety Risk Prediction

Improved BiLSTM Model For Online Food Safety Risk Prediction

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Zekai Zhang Mao Li Jun Sun Qidong Chen School Of Artificial Intelligence and Computer Science Jiangnan University WuXi China

ISBN: (纸本)9781665428903

The prediction of security risk is a new problem in the field of modeling. The value of food safety risk is dynamic, non-linear and non-stationary. In order to improve the prediction accuracy of non-stationary sequences, this paper proposes an improved time series prediction model of bidirectional long short-term memory network based on NS-ADAM optimization algorithm. By using NS-ADAM optimization algorithm, the non-stationary series can be approximately regarded as stationary series in a fixed window length. Combined with the advantages of two-way long-term and short-term network model to strengthen the correlation between current data and historical data, the combined network model significantly improves the prediction accuracy of non-stationary time series. The experimental results show that compared with the traditional integrated moving average autoregressive model and the conventional long-term and short-term memory network model, the improved model has higher prediction accuracy and better fitting effect for non-stationary series.

关键词： Computational modeling Heuristic algorithms Time series analysis Predictive models Prediction algorithms Approximation algorithms Data models

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Homomorphic Encryption based Federated Learning for Financial Data Security

Homomorphic Encryption based Federated Learning for Financia...

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computing and Communication Systems (I3CS), international Conference on

作者： Shalini Dhiman Sumitra Nayak Ganesh Kumar Mahato Anil Ram Swarnendu Kumar Chakraborty Department of Computer Science & Engineering National Institute of Technology Arunachal Pradesh India

Federated Learning is a distributed machine learning technique that enables on-device training without exchanging the sensitive data over the centralized server. In this paper, Federated Learning is used to train financial data models on-devices with the help of IoT applications in financial or business systems. This creates more advanced and secured financial business models. We have applied the mechanism of homomorphic encryption and cryptographic primitives, including masking and local model protection to prevent any kind of inferring in private financial or business data where multiple attackers usually find a way of model inversion or reconstruction attack. We used datasets of various financial sectors as the primary measure, rather than taking the size of datasets that is generally used in deep learning, to get the correct measurement of the rate of contribution in every session of the local model’s training to the training of the global model. If the calculated online number of clients exceeds a predetermined threshold, then the federated learning process will be continued with the dropout-tolerant plan. The security study demonstrates that the suggested solution fulfils data privacy requirements. The costs of computation and communication are also examined theoretically. According to research observations, the proposed approach achieved promising outcomes while assuring privacy preservation when compared to existing schemes.

关键词： Training Deep learning Data privacy Federated learning Computational modeling Size measurement Data models

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Research Progress, Hot Spots and Prospects in the Field of Agricultural Innovation in China: —CiteSpace analysis based on 2258 articles

Research Progress, Hot Spots and Prospects in the Field of A...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Shuqi Zhu Qian Lu Yun Xiang School of Business Guilin University of Electronic Technology Guilin People's Republic of China Lingnan College Sun Yat-sen University Guilin People's Republic of China

ISBN: (纸本)9781665428903

Agricultural innovation is an important force driving the high-quality development of agriculture. Based on 2258 articles in China National Knowledge Infrastructure(CNKI) core journal database, and using Cite-Space software, we investigated the research progress, research hotspots and changes in the field of agricultural innovation in China from 2005 to 2020, trying to clarify the research trends and frontiers in the field of agricultural innovation. The results show that: ① The number of publications in the field of agricultural innovation in China presents an overall trend of “increasing volatility in the early stage, a short-term decline in the medium-term, and a rapid rebound in the near term”, but the cooperative research between authors and institutions is relatively weak; ② The hotspots of agricultural innovation research focus on technology Innovation, institutional innovation, innovation evaluation and modern agriculture, etc., and show phase characteristics; ① The evolution of research hot topics in the field of agricultural innovation in China reflects a strong background of the times. Therefore, in the future, interdisciplinary research will be a new hotspot and direction in the field of agricultural innovation. It is urgent to carry out systematic research on theories, methods, and applications, and strengthen the research on the integration of agricultural innovation and practice.

关键词： Technological innovation Systematics Databases Force Market research Agriculture Software

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