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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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Failure order: A missing piece in disk failure processing of data centers 21

Failure order: A missing piece in disk failure processing of...

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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： Yi, Yusheng Xiao, Jiang Wu, Song Li, Huichuwu Jin, Hai National Engineering Research Center for Big Data Technology System Services Computing Technology System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728120584

To avoid data loss, data centers adopt disk failure prediction (DFP) technology to raise warnings ahead of actual disk failures, and process the warnings in the order they are raised, i.e., a first-in-first-out (FIFO) warning order. The FIFO-guided warning order can process warnings timely when disk failures are rare in data centers. With the growing scale of data centers, the increasing number of disk failures leads to a complex situation that multiple warnings are raised simultaneously, where the FIFO-guided warning order neither processes warnings timely, nor manages warnings properly due to lack of the priority of warnings. Thus, a real-time and finer-grained priority guidance for warning order management is an urgent need. To this end, we turn our attention to the failures since each warning corresponds to a fail event. The key insight is that the interdependence of failures, i.e., the order failure occurred, indicates the order of warning processing. With an accurate failure order, data centers can decrease the probability of data loss and the downtime of latency-sensitive applications by processing urgent warnings in advance. In this paper, we predict the failure order with a LambdaMART model, which is a state-of-the-art ranking algorithm in information retrieval. To avoid overly concerning on the correctness of high-rank warnings in information retrieval, we design a symmetric metric to evaluate the prediction evaluation of failure order. Experiment on a public dataset, provided by the Backblaze company, shows that our model outperforms the FIFO order and the order from previous DFP models. © 2019 IEEE.

关键词： Information retrieval

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An Edge-Fog computing Framework for Cloud of Things in Vehicle to Grid Environment

An Edge-Fog Computing Framework for Cloud of Things in Vehic...

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IEEE international symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Neeraj Kumar Tanya Dhand Anish Jindal Gagangeet Singh Aujla Haotong Cao Longxiang Yang Computer Science & Engineering Department Thapar Institute of Engineering & Technology Patiala India School of Computer Science and Electronic Engineering University of Essex Colchester UK School of Computing Newcastle University Newcastle upon Tye UK College of Telecommunications & Information Engineering Nanjing Universiy of Posts and Telecommunications China

ISBN: (数字)9781728173740

ISBN: (纸本)9781728173757

The penetration of electric vehicles (EVs) embedded with information and communication technology (ICT) devices and tools form a huge connected network that can be viewed as Internet-of-EVs(IoEV). The huge data gathered in IoEV network needs to be processed at cloud-based infrastructure which has abundant resources. However, due to the high mobility of the EVs, resource management from the remote cloud service providers has become one of the most difficult tasks to be performed in this environment. In this regard, data analytics fused with fog or edge computing can be leveraged to increase the resource availability in V2G environment where resources are provided to the EVs on the edge of the network. Keeping these points in mind, this paper presents a new framework for integration of cloud computing and IoEV on the edge of the network which provides flexibility to the end users for smooth execution of various applications. In addition, a resource allocation and job scheduling strategy for EVs at the edge of the network is presented in the paper. The results obtained with respect to various performance metrics confirm the applicability of the proposed scheme for future applications in V2G scenario.

关键词： Edge computing Energy exchange Vehicle-to-grid Charging stations Cloud computing Resource management Computational modeling

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An efficient GPU implementation technique for higher-order 3D stencils 21

An efficient GPU implementation technique for higher-order 3...

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作者： Anjum, Omer Simon, Garcia De Gonzalo Hidayetoglu, Mert Hwu, Wen-Mei Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignIL United States

ISBN: (纸本)9781728120584

stencils are a family of widely used computational patterns that play a critical role in various scientific and engineering applications. stencil computations are known to be memory-bandwidth bound, thus a number of different techniques and algorithms that optimizes memory bandwidth usage have been proposed. However, existing techniques fall short in addressing the needs of large stencils, particularly more advanced stencil patterns involving non-axis aligned grid points. To handle non-axis aligned grid points, existing methods either use 3D caching or 2D caching schemes with more than one pass over the stencil per iteration, which suffers from the high intensity of memory accesses. The large number of memory accesses in these methods hinder the available performance. In this work, we present a new GPU-based implementation technique called 'SWiC' that focuses on using 2D caching to efficiently implement advanced 3D stencil patterns, involving non-axis aligned grid points, and reducing global memory transactions by increased data reuse while only requiring a single pass per iteration. In contrast to the current approaches that maintain input register queues, the proposed approach maintains and updates the output register queue instead. The analysis shows that SWiC achieves a significant reduction in memory transactions which translates to a significant application speedup, 1.6x to 5.76x, when compared to the current state-of-the-art GPU stencil implementation. 'SWiC' was evaluated across the latest three Nvidia GPU architectures as of the writing of this paper, as well as various stencil patterns and sizes. We also show that 'SWiC' does not suffer from performance penalties when applied to simpler 3D stencils without non-axis aligned grid points, covering a wide application range. When running on a multi-node setting, we study the scaling efficiency of SWiC and show that it is able to achieve a weak scaling efficiency of about 96%. © 2019 IEEE.

关键词： Graphics processing unit

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Edgestore: Towards an edge-based distributed storage system for emergency response 21

EdgeStore: Towards an edge-based distributed storage system ...

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作者： Rashid, Md Tahmid Zhang, Daniel Wang, Dong Department of Computer Science and Engineering University of Notre Dame Notre DameIN United States

ISBN: (纸本)9781728120584

With the pervasive influence of rich sensing technologies and internet connectivity, Social Sensing based Edge computing (SSEC) is gradually becoming the forefront of context-aware sensing applications in smart cities. These applications generate huge volumes of data which require a sustainable storage platform. While storing sensing data into the cloud can be an immense benefit, cloud-based distributed storage requires constant internet connectivity, which deems the platform unusable in emergency situations with disrupted internet connectivity. This paper introduces the idea of utilizing privately owned edge devices for a decentralized distributed data storage during emergency situations. However, persuading the edge devices to cooperate and participate in sharing storage brings about an unprecedented set of challenges. The first challenge lies in the inconsistent availability of the storage system as the edge devices may not be inclined to share storage resource. The second challenge lies in configuring all the edge devices to integrate with the storage system in a scalable manner. In this paper, we present Edgestore, an edge-based distributed storage system that attempts to address the above challenges by introducing a game-theoretic resource incentivizing framework. We implement a prototype of Edgestore on top of the Linux Ext4 file system using the FUSE interface. The results on two real world case studies demonstrate that Edgestore provides significant performance improvements over the state-of-the-art distributed storage systems in terms of throughput, energy consumption and latency. © 2019 IEEE.

关键词： Game theory

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Research on the 2-good-neighbor conditional diagnosability of augmented cubes 21

Research on the 2-good-neighbor conditional diagnosability o...

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作者： Guo, Yang Chen, Miaojiang Liang, Jiarong Xie, Min School of Computer and Electronic Information Guangxi University Nanning China

ISBN: (纸本)9781728120584

Diagnosability is an important metric measuring the reliability of multiprocessor system. The classical problem of fault diagnosis is discussed widely and the diagnosability of many well-know networks have been further explored. Fault diagnosis of the system restrains that every free-fault nodes has at least g free-fault neighbor vertices, which is called the g-good-neighbor conditional diagnosis. As a famous topology structure of interconnection networks, the n-dimensional augmented cubes have many good properties. By analyzing and demonstrating the properties of 2-good-neighbor conditional diagnosis for augmented cubes, this paper first proves that the 2-good-neighbor conditional diagnosability of augmented cubes under the MM∗ model is 8n-18. The result shows that the 2-good-neighbor conditional diagnosability of augmented cubes is larger than its classical diagnosability 2n-1. © 2019 IEEE.

关键词： Failure analysis

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Attention-based neural network: A novel approach for predicting the popularity of online content 21

Attention-based neural network: A novel approach for predict...

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作者： Nguyen, Minh-Tri Le, Duong H. Nakajima, Takuma Yoshimi, Masato Thoai, Nam Faculty of Computer Science and Engineering Ho Chi Minh University of Technology Vietnam National University Ho Chi Minh City Viet Nam Service Strategy Sector Shinjuku-ku Tokyo Japan Strategic Technology Center TIS Inc. Shinjuku-ku Tokyo Japan

ISBN: (纸本)9781728120584

Since the rate at which new content is uploaded to the Internet has reached unprecedented marks, knowing the popularity of online content, especially video, is of importance for network management, recommendation schemes, service design, advertising planning, and so on. Despite the fact that various models have been developed, few of them address the short-term popularity prediction. Toward this goal, we exploit the self-attention mechanism of the Transformer, a state-of-the-art model in neural machine translation, to forecast the values of multiple time series in the near future. Specifically, we propose an attention-based non-recursive neural network, a novel model that entirely dispenses with recurrence and convolutions, for time series prediction. Since our model is the combination of the input attention mechanism in the dual-stage attention-based recurrent neural network (DA-RNN) and the self-attention of Transformer, it is able to adaptively select the most relevant input sequences as well as capture the long-term dependencies across previous time steps to make the prediction. The experiments show the root mean square errors (RMSEs) achieved by our model are only 6.06 and 3.60 when testing on the NASDAQ 100 dataset and the views count of the top most popular videos on Youtube respectively, while the RMSEs of DA-RNN are 8.52 and 12.31. Hence, our model outperforms the baseline not only in time series prediction but also in contents popularity prediction aspect. © 2019 IEEE.

关键词： Recurrent neural networks

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A distributed class-incremental learning method based on neural network parameter fusion 21

A distributed class-incremental learning method based on neu...

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作者： Guan, Hongtao Wang, Yijie Ma, Xingkong Li, Yongmou National Laboratory for Parallel and Distributed Processing College of Computer Science and Technology National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781728120584

Class-incremental learning has received considerable attention due to the better adaptability to constantly changing characteristic of online learning. The neural network is suitable for class-incremental learning because of batch training style and fine-tuning. However, as the rapid growth of data centers and data category, data isolated island problem presents a new challenge to the current machine learning model: how to provide an accurate training method in a distributed and incremental manner. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIL, which iteratively updates new class parameters and fixes existing class parameters to achieve high training accuracy. Especially, we propose MF, a parameter fusion method to integrate the parameters of multi-nodes into a new global parameter. In order to match the requirements of identical columns corresponding to the known classes, we propose a single node class-incremental learning, called SCIL. To evaluate the performance of DCIL, we conduct experiments on 5 datasets under various parameter settings on both 2 and 4 nodes distributed scenarios. Extensive experiments confirm that DCIL outperform the general baselines and achieve classification accuracy closes to the method of data aggregating. © 2019 IEEE.

关键词： Iterative methods

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Scaling Productivity and Innovation on the Path to Exascale with a "Team of Teams" Approach 6th

Scaling Productivity and Innovation on the Path to Exascale ...

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6th international Conference on Business, Government, and Organizations (HCIBGO) held as part of 21st international Conference on Human-computer Interaction (HCII)

作者： Raybourn, Elaine M. Moulton, J. David Hungerford, Aimee Sandia Natl Labs Albuquerque NM 87185 USA Los Alamos Natl Labs Los Alamos NM 87545 USA

ISBN: (纸本)9783030223380;9783030223373

One of the core missions of the Department of Energy (DOE) is to move beyond current high performance computing (HPC) capabilities toward a capable exascale computing ecosystem that accelerates scientific discovery and addresses critical challenges in energy and national security. The very nature of this mission has drawn a wide range of talented and successful scientists to work together in new ways to push beyond the status-quo toward this goal. For many scientists, their past success was achieved through efficient and agile collaboration within small trusted teams that rapidly innovate, prototype, and deliver. Thus, a key challenge for the ECP (Exascale computing Project) is to scale this efficiency and innovation from small teams to aggregate teams of teams. While scaling agile collaboration from small teams to teams of teams may seem like a trivial transition, the path to exascale introduces significant uncertainty in HPC scientific software development for future modeling and simulation, and can cause unforeseen disruptions or inefficiencies that impede organizational productivity and innovation critical to achieving an integrated exascale vision. This paper identifies key challenges in scaling to a team of teams approach and recommends strategies for addressing them. The scientific community will take away lessons learned and recommended best practices from examples for enhancing productivity and innovation at scale for immediate use in modeling and simulation software engineering projects and programs.

关键词： high performance computing (HPC) Exascale Team of teams Computational science & engineering (CSE) VUCA Organizational productivity

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A parallel BMH string matching algorithm based on OpenMP 21

A parallel BMH string matching algorithm based on OpenMP

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作者： Qi, Xiao Liu, Bin Li, Yuancheng Du, Yanning Li, Yuxiang Niu, Dangdang College of Information Engineering Northwest A AND F University Yangling China School of Computer Science and Technology Xi'An University of Science and Technology Xi'an China

ISBN: (纸本)9781728120584

BMH(Boyer-Moore-Horspool) string matching algorithms have played an important role in the field of biological sequence alignment, text processing, spell checking, and computer virus signature matching. However, with the explosive growth of the data, the serial string matching algorithm is too slow to finish the matching task within an acceptable time. This paper proposed a parallel BMH string matching algorithm based on OpenMP. The parallelism of the BMH algorithm is first identified by theoretical analysis. Then, the sequential algorithm is designed as a parallel algorithm in a data-parallel form and the larger string matching task is divided into multiple sub-string matching tasks by partitioning strategy. Furthermore, using the thread-level parallel technique, each thread carries out string matching operations on different sub-string blocks in parallel. Compared with the serial BMH algorithm, the parallel BMH matching algorithm with 8 threads can achieve up to 3.53 times speedup. On the OpenMP platform, experimental results show that the proposed parallel algorithm can acquire a significant increase in speedup. Under the accuracy of ensuring the string matching, the optimal number of threads and the most optimal block segmentation scheme are obtained through experimental tests. It indicates that the OpenMP-based parallel BMH algorithm has excellent acceleration performance, and an advantageous application prospect in various field. © 2019 IEEE.

关键词： string searching algorithms

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Verification of performance of Multi-Parental Real-Valued Crossover in Interactive Genetic Algorithm

Verification of performance of Multi-Parental Real-Valued Cr...

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international symposium on Soft computing and Intelligent Systems (SCIS)

作者： Yusuke Yabuzaki Yoshiko Hanada Makoto Fukumoto Graduate School of Science and Engineering Kansai University Osaka Japan Faculty of Engineering Science Kansai University Osaka Japan Computer Science and Engineering Fukuoka Institute of Technology Fukuoka Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

In interactive evolutionary computation, it is important to reduce user fatigue and to enhance the search performance in a few generations, since fatigue has an impact on users' evaluation process. These issues also have been found in in interactive genetic algorithm (IGA). In this study, we adopt multi-parental unimodal normal distribution crossover (UNDX- m) to solve these problems in IGA. UNDX- m generates many offspring with considering the distribution of selected parents. The next population can be generated by applying UNDX- m once with more than two parent solutions. Through this mechanism, the evaluation process becomes simple manner where users only select their preferable solutions. In addition, UNDX- m can generate offspring that inherit their parents' traits, which enhance the search quality. In this paper, we evaluate the effectiveness of UNDX- m through the numerical experiment, by using a color combination problem in Web design.

关键词： Color Fatigue Gaussian distribution statistics standards Sociology Genetic algorithms

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