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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

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Distributed differentially private matrix factorization based on ADMM 21

Distributed differentially private matrix factorization base...

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21st IEEE international Conference on high performance computing and Communications, 17th IEEE international Conference on Smart City and 5th IEEE international Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Xue, Erkang Guo, Ruixin Zhang, Feng Wang, Lizhe Zhang, Xiaohan Qu, Guangzhi School of Computer Science China University of Geosciences Wuhan China Department of Engineering and Computer Science Oakland University Rochester United States

ISBN: (纸本)9781728120584

Matrix factorization (MF) is an essential technique to implement intelligent recommender systems widely applied in industry. Privacy and efficiency are two essential issues concerning MF. We leverage two techniques, differential privacy (DP) and distributed computing, to address the two concerns, respectively. (1) Differentially private MF is still challenging since conventional strategies lead to significant error accumulation;we adopt the objective function perturbation technique to tackle such a challenge. (2) We adopt the alternating direction method of multipliers (ADMM) framework to parallelize the factorization to improve performance;to implement this parallelization, we adopt the effective matrix split method and introduce a novel integration strategy for distributed DP based on the post-processing theorem. We identify our work as distributed differentially private MF based on ADMM. The novelty of the work rests in that it is the first to successfully integrate the two innovative techniques to address both privacy and efficiency for MF. We establish the mathematical model and conduct experiments to validate the soundness of our idea. The experimental results based on industrial datasets show that the distributed differentially private MF algorithm provides scalable speedup performance within a limited precision loss while preserving user privacy. © 2019 IEEE.

关键词： Collaborative filtering

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Task scheduling in mobile edge computing with stochastic requests and m/m/1 servers 21

Task scheduling in mobile edge computing with stochastic req...

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作者： Yuchong, Luo Jigang, Wu Yalan, Wu Long, Chen School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781728120584

A multi-user mobile edge computing system with stochastic requests and M/M/1 queuing based servers is proposed in this paper. The problem of minimizing the total response time of all tasks is formulated, which is proved to be NP-complete. A greedy algorithm is proposed to solve the mentioned optimization problem, which prefers to assign task to the server with minimum response time for the task. Simulation results show that, the average response time of tasks in the proposed greedy algorithm is saved by 20%-30%, in comparison to the proposed random algorithm. Meanwhile, the average response time of tasks in customized tabu search is decreased by 8.5%, compared to the greedy algorithm. © 2019 IEEE.

关键词： Mobile edge computing

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A comparative study of large-scale cluster workload traces via multiview analysis 21

A comparative study of large-scale cluster workload traces v...

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作者： Ruan, Li Xu, Xiangrong Xiao, Limin Yuan, Feng Li, Yin Dai, Dong State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Institute of Software Application Technology Guangzhou AND Chinese Academy of Sciences Guangzhou China Department of Computer Science University of North Carolina Charlotte CharlotteNC United States

ISBN: (纸本)9781728120584

Understanding the characteristics of workloads of the large-scale Clusters is the key to diagnose the system bottlenecks, making optimal configuration decisions, improving the system throughput and resource usage. Due to the diversity and multiview of the workload traces, featuring the good designs and bottlenecks by analyzing the workloads under realworld scenarios becomes increasingly challenging. This paper introduces a multiview based trace comparative analysis method by comparatively characterizing how the architecture, jobs, tasks, machines, and resources usage were managed among different platforms. A case study is performed which verified the effectiveness of our method by comparatively analyzing two most representative big traces: Google trace and Alibaba 2018 trace. Quantitative findings, together with the performance bottleneck inferences and suggestions are also presented. To the best of our knowledge, we are the first to perform such comparative empirical study on these two traces using a multiview based approach. Our multifaceted analyses and new findings not only reveal insights that we believe are useful for system designers, IT practitioners and users, but also can promote more researches on big trace data comparative analysis in large-scale clusters. © 2019 IEEE.

关键词： Trace analysis

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Redesign NAMD molecular dynamics non-bonded force-field on sunway manycore processor 21

Redesign NAMD molecular dynamics non-bonded force-field on s...

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作者： Chen, Junshi An, Hong Yan, Binghui Liang, Weihao Liu, Xin Department of Computer Science and Technology University of Science and Technology of China China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781728120584

NAnoscale Molecular Dynamics (NAMD) is a parallel molecular dynamics application used to study the structure and dynamics of large biomolecular complexes, such as various types of viruses. In a biomolecular system, billions of time steps are required to understand the biological phenomena of interest. It requires enormous computing power to have a long-timescale observation of the molecule behavior of a large simulation system. NAMD meets this demand with thousands of state-of-the-art manycore processors. The Sunway Taihulight supercomputer is the number one computing system in the top 500 list of supercomputers in November 2017, with a peak performance of 125 PFlops and equipped with 40,960 SW26010 manycore processors. In this paper, we present various optimization techniques that enable the non-bonded force calculation, which is the most computation intensive and time-consuming part of NAMD, to achieve high performance on the SW26010 manycore processor. The optimizations include software cache design on local data memory, separated computing and storage cores, fine-grained inter-core data communication for fast force updating, and vectorization. © 2019 IEEE.

关键词： Molecular dynamics

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Modeling HPC storage performance using long short-term memory networks 21

Modeling HPC storage performance using long short-term memor...

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作者： He, Youbiao Dai, Dong Bao, Forrest Sheng Department of Computer Science Iowa State University AmesIA United States Department of Computer Science University of North Carolina at Charlotte CharlotteNC United States

ISBN: (纸本)9781728120584

As scientific applications become increasingly data-intensive, the I/O and storage components can easily become the bottlenecks of high performance computing (HPC) systems. Such a trend drives recent development of complex, layered HPC I/O software stack, which contains a huge number of tunable parameters, to achieve good I/O performance. It then becomes necessary for the application developers to fully understand the performance projection of their applications given a set of chosen parameters, and also their potential effects on other concurrent applications. Although there are a number of recent studies aiming to model the relationship between I/O performance of applications and their parameters to provide such insights, the complexity of HPC systems and the interactions between concurrent applications make existing approaches over-simplified and far from practical. In this paper, we try to solve this problem using a powerful deep learning tool, the long short-term memory (LstM), together with the full history of I/O settings of all concurrent applications in the system. To prove the concept, we carry out extensive experiments using the IOR benchmark and train the model using real system logs. Empirical results show that our LstM-based model can predict the write speed more accurately than the state-of-the-art approaches. We hope this work can initiate the study toward more accurate and practical I/O performance estimation in HPC systems. © 2019 IEEE.

关键词： Long short-term memory

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Data reduction through decision-making based on event-sensitivity in iot-enabled event monitoring 21

Data reduction through decision-making based on event-sensit...

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作者： Bhuiyan, Md Zakirul Alam Wang, Tian Zaman, Aliuz Wang, Guojun School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China Department of Computer Science and Technology Huaqiao University Xiamen Fujian361021 China Department of Computer and Information Sciences Fordham University New YorkNY10458 United States

ISBN: (纸本)9781728120584

The Internet of Things (IoT) are being adopted to improve the efficiency and reliability of diverse monitoring applications, including critical events (such as fire, structural damage, crack) in the smart city's public safety application. In this paper, we present BigReduce, an IoT-enabled scheme to reduce a big quantity of data through decision-making when acquiring data and transferring the acquired data throughout the IoT network, while providing event sensitivity in industrial environments. At first, BigReduce executes decision-making briefly on the ongoing collected data in a discrete-time based on the event sensitivity so that data acquisition regarding the event-insensitivity is reduced. Then, BigReduce executes decisionmaking locally at the IoT devices to decide whether or not transmitting only a part of the gathered data that have a high event-sensitivity so that data with event-insensitivity is reduced. At an upstream IoT device (say cluster head), BigReduce runs a distributed decision-making to verify whether or not the collected data have to be transferred. Outcomes achieved through a proof-of-concept implementation shows that BigReduce is capable of reducing data quantity by 87% and energy cost by at least 82% compared to related schemes. © 2019 IEEE.

关键词： Internet of things

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EdgeCache: A game-theoretic edge-based content caching system for crowd video sharing 21

EdgeCache: A game-theoretic edge-based content caching syste...

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作者： Vance, Nathan Zhang, Daniel Wang, Dong Department of Computer Science and Engineering University of Notre Dame Notre DameIN United States

ISBN: (纸本)9781728120584

As sensing technologies become more prevalent, it has become increasingly common for edge and Internet of Things (IoT) devices to produce and consume vast quantities of data. Unfortunately, the massive increase in data sharing has far outpaced the growth of networking capabilities, thus necessitating a shift away from the classic client/server relationship to distributed systems. Past works have helped to address this problem though the use of fog computing, however, such solutions continue to suffer from the server becoming a bottleneck. In this paper, we develop EdgeCache, an edge computing system that employs game theory to optimally process and cache data by leveraging the computational, storage, and networking capabilities of devices that participate in the system. We implement EdgeCache in a crowd video sharing application that entails scalability, Quality of Experience (QoE), and time-sensitivity constraints. Our results demonstrate that EdgeCache is capable of delivering superior performance while consuming fewer network resources compared to state-of-the-art baselines. © 2019 IEEE.

关键词： Edge computing

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A lightweight collective communication based parallel algorithm for the greedy point selection in RBF mesh deformation 21

A lightweight collective communication based parallel algori...

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作者： Zhao, Ran Li, Chao Guo, Xiaowei Fan, Sijiang Wang, Yi Liu, Yi Yang, Canqun State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781728120584

The greedy algorithm is one of the most efficient point selection method utilized by radial basis function (RBF) based mesh deformation in computational fluid dynamic (CFD). To improve the computational efficiency of the greedy process, several parallelization methods are developed in the past few years for the data reduction. However, these methods produce additional time cost because of the communication redundancy which can restrict the parallel scale. This paper proposes a new parallel strategy for the greedy selection in which a lightweight collective communication is adopted to replace the point-to-point communication. In addition, this strategy emphasizes that all the parallel processes operate under the same computational complexity as much as possible to reinforce the load balancing. Specifically, a three-dimension undulating fish model is utilized to validate the lightweight collective communication strategy. And finally, the proposed one gets up to 20% time performance improvement on average compared with two classic parallel methods. © 2019 IEEE.

关键词： Data reduction

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Improving the performance of mongodb with RDMA 21

Improving the performance of mongodb with RDMA

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作者： Lu, Fan Fang, Tao Zhang, Ziyu Li, Sen Chen, Junshi An, Hong Han, Wenting School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781728120584

MongoDB is a NoSQL database which stores the data in form of key-value pairs. It is an open-source, document database which is being used in many data-center applications (e.g. Google, Facebook, etc.) because of its high performance, high availability and automatic scaling. For this kind of data intensive applications, low latency and high throughput are extremely important. However, the existing MongoDB is built upon Boost. Asio, which is a cross-platform C++ library for network and low-level I/O. It can provide a great degree of portability, but at the price of performance due to the limitation of Ethernet network and TCP/IP protocol. This makes MongoDB hard to meet the performance requirements of data intensive applications. The high performance computing(HPC) domain has developed high performance networks such as InfiniBand for many years, which provides higher bandwidth and lower latency than Ethernet. These kind of networks also provide advanced features, such as Remote Direct Memory Access(RDMA), to achieve better performance. In this paper, we present a modern RDMA capable design of MongoDB. The performance evaluation on QDR (32 Gbps) shows that our RDMA design achieves 2.84X and 1.93X peak speedup over 1 Gigabit Ethernet (1 GigE) and IP-over-InfiniBand (IPoIB) in all experiments. To the best of our knowledge, this is first MongoDB design utilizing high performance RDMA capable interconnects. © 2019 IEEE.

关键词： Ethernet

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Comprehensive evaluation of large-scale parallel matrix factorization algorithms 21

Comprehensive evaluation of large-scale parallel matrix fact...

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作者： Liu, Xiaolong Ho, Ching-Yu Zheng, Sifei Yuan, Shyan-Ming College of Computer and Information Sciences Digital Fujian Institute of Big Data for Agriculture and Forestry Fujian Agriculture and Forestry University Fuzhou350002 China Department of Computer Science National Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781728120584

Fast parallel stochastic gradient descent (FPSG) and non-locking stochastic multi-machine (NOMAD) are two representative matrix factorization algorithms for real-time accurate recommendation of large-scale recommender systems. To fairly ascertain the performance of FPSG and NOMAD matrix factorization algorithms, a comprehensive evaluation is presented in this paper. streaming SIMD extensions (SSE) and different learning rate schedules are applied to evaluate the convergence performance of these two algorithms. The experimental results show that SSE can speed up the convergence performance of original NOMAD algorithm in Netflix dataset. In addition, no matter what learning rate schedules are used, FPSG algorithm has relatively good convergence results than NOMAD under the same parameters and experimental environments. © 2019 IEEE.

关键词： Recommender systems

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