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Accelerating GPGPU architecture simulation 13

Accelerating GPGPU architecture simulation

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Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems

作者： Zhibin Yu Lieven Eeckhout Nilanjan Goswami Tao Li Lizy John Hai Jin Chengzhong Xu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China ELIS Department Ghent University Ghent Belgium Intelligent Design of Efficient Architectures Lab University of Florida Gainesville USA Department of Electrical and Computer Engineering University of Texas at Austin Austin USA Service Computing Technologies and System Lab/Cluster and Grid Computing Lab HUST Wuhan China Department of Electrical and Computer Engineering Wayne State University Detroit USA

ISBN: (纸本)9781450319003

Recently, graphics processing units (GPUs) have opened up new opportunities for speeding up general-purpose parallel applications due to their massive computational power and up to hundreds of thousands of threads enabled by programming models such as CUDA. However, due to the serial nature of existing micro-architecture simulators, these massively parallel architectures and workloads need to be simulated sequentially. As a result, simulating GPGPU architectures with typical benchmarks and input data sets is extremely time-consuming. This paper addresses the GPGPU architecture simulation challenge by generating miniature, yet representative GPGPU kernels. We first summarize the static characteristics of an existing GPGPU kernel in a profile, and analyze its dynamic behavior using the novel concept of the divergence flow statistics graph (DFSG). We subsequently use a GPGPU kernel synthesizing framework to generate a miniature proxy of the original kernel, which can reduce simulation time significantly. The key idea is to reduce the number of simulated instructions by decreasing per-thread iteration counts of loops. Our experimental results show that our approach can accelerate GPGPU architecture simulation by a factor of 88X on average and up to 589X with an average IPC relative error of 5.6%.

关键词： micro-architecture simulation performance general purpose graphics processing unit (GPGPU)

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Optimizing data shuffling in data-parallel computation by understanding user-defined functions 9

Optimizing data shuffling in data-parallel computation by un...

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9th USENIX Symposium on Networked systems Design and Implementation, NSDI 2012

作者： Zhang, Jiaxing Zhou, Hucheng Chen, Rishan Fan, Xuepeng Guo, Zhenyu Lin, Haoxiang Li, Jack Y. Lin, Wei Zhou, Jingren Zhou, Lidong Microsoft Research Asia China Microsoft Bing United States Peking University China Services Computing Technologyand System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Georgia Institute of Technology United States

Map/Reduce style data-parallel computation is characterized by the extensive use of user-defined functions for data processing and relies on data-shuffling stages to prepare data partitions for parallel computation. Instead of treating user-defined functions as "black boxes", we propose to analyze those functions to turn them into "gray boxes" that expose opportunities to optimize data shuffling. We identify useful functional properties for userdefined functions, and propose SUDO, an optimization framework that reasons about data-partition properties, functional properties, and data shuffling. We have assessed this optimization opportunity on over 10,000 dataparallel programs used in production SCOPE clusters, and designed a framework that is incorporated it into the production system. Experiments with real SCOPE programs on real production data have shown that this optimization can save up to 47% in terms of disk and network I/O for shuffling, and up to 48% in terms of cross-pod network traffic. © 2012 by The USENIX Association. All Rights Reserved.

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A control mechanism about quality of service for resource management in simulation grid

A control mechanism about quality of service for resource ma...

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4th International Conference on Computational and Information Sciences, ICCIS 2012

作者： Kuang, Ping Jin, Hai Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769547893

The simulation grid has become a very important research topic. In order to ensure the QoS of resource management, we present a control mechanism in simulation grid, which is based on the prediction methods. In the control mechanism, we first introduce our resource management module in simulation grid. Then, we propose a resource allocation algorithm. In order to justify the feasibility and the availability of this control mechanism, a series of experiments have been done. The results show that it is feasible to schedule the system resources and control the QoS of system resources for tasks in simulation grid. © 2012 IEEE.

关键词： Quality of service

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An efficient distributed transactional memory system

An efficient distributed transactional memory system

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11th IEEE International Conference on Trust, Security and Privacy in computing and Communications, TrustCom-2012

作者： Peng, Xin Liao, Xiaofei Jin, Hai Fan, Xuepeng Tu, Xuping Yu, Linchen Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769547459

Transactional memory (TM) is a parallel programming concept which reduces challenges in parallel programming. Existing distributed transactional memory system consumes too much bandwidth and brings high latency. In this work, we present Transactional Memory system for cluster (Clustm), a generalized and scalable distributed transactional memory system. Our system addresses several open issues posed by this domain, including transactional memory consistency protocol, cache consistency protocol, and the distribution strategy of the metadata of shared data across the cluster. Then, we evaluate our design with several workloads, and the results demonstrate outstanding performance. © 2012 IEEE.

关键词： Parallel programming

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Towards Privacy Preserving Mining over Distributed Cloud Databases

Towards Privacy Preserving Mining over Distributed Cloud Dat...

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International Conference on Cloud and Green computing (CGC)

作者： Ayad Ibrahim Hai Jin Ali A. Yassin Deqing Zou Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan China

Due to great advances in computing and Internet technologies, organizations have been enabled to collect and generate a large amount of data. Most of these organizations tend to analyze their data to discover new patterns. Usually, analyzing such amount of data requires huge computational power and storage facilities that may not be available to these organizations. Cloud computing offers the best way to solve this problem. Storing the private data of different organizations in the same cloud server enhances the mining process, but at the same time, raises privacy concerns. Therefore, it is highly recommended to support privacy preserving data mining algorithms in the cloud environment. This paper introduces an efficient and accurate cryptography-based scheme for mining the cloud data in a secure way without loss of accuracy. Specifically, we address the problem of K-nearest neighbor (KNN) classification over horizontally distributed databases without revealing any unnecessary information. We have utilized the recently developed cryptography primitive, order preserving symmetric encryption (OPSE), to integrate securely the local classifications at a lower cost than the previously presented privacy preserving data mining schemes. Empirical results on real datasets demonstrate that the proposed scheme has similar performance with the naive mining systems in terms of classification accuracy.

关键词： Cryptography Vectors Distributed databases Accuracy Training Data privacy

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Secure Rank-Ordered Search of Multi-keyword Trapdoor over Encrypted Cloud Data

Secure Rank-Ordered Search of Multi-keyword Trapdoor over En...

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IEEE Asia-Pacific Conference on services computing (APSCC)

Advances in cloud computing and Internet technologies have pushed more and more data owners to outsource their data to remote cloud servers to enjoy with huge data management services in an efficient cost. However, despite its technical advances, cloud computing introduces many new security challenges that need to be addressed well. This is because, data owners, under such new setting, loss the control over their sensitive data. To keep the confidentiality of their sensitive data, data owners usually outsource the encrypted format of their data to the untrusted cloud servers. Several approaches have been provided to enable searching the encrypted data. However, the majority of these approaches are limited to handle either a single keyword search or a Boolean search but not a multikeyword ranked search, a more efficient model to retrieve the top documents corresponding to the provided keywords. In this paper, we propose a secure multi-keyword ranked search scheme over the encrypted cloud data. Such scheme allows an authorized user to retrieve the most relevant documents in a descending order, while preserving the privacy of his search request and the contents of documents he retrieved. To do so, data owner builds his searchable index, and associates with each term document with a relevance score, which facilitates document ranking. The proposed scheme uses two distinct cloud servers, one for storing the secure index, while the other is used to store the encrypted document collection. Such new setting prevents leaking the search result, i.e. the document identifiers, to the adversary cloud servers. We have conducted several empirical analyses on a real dataset to demonstrate the performance of our proposed scheme.

关键词： Servers Indexes Encryption Privacy Cloud computing

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A comprehensive monitoring framework for virtual computing environment

A comprehensive monitoring framework for virtual computing e...

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International Conference on Information Networking

作者： Guofu Xiang Hai Jin Deqing Zou Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Virtualization can divide or aggregate the underlying resource flexibly, and it attracts attention from both academic and industry in recent years. Guest operating systems in virtual machines can be various, and the states of virtual machines changes all the time. Due to the complexity of virtual computing environment, it brings tremendous challenges for security monitoring. Existing monitoring mechanism can not adapt to the dynamic and diversity of virtual machines. Therefore, a comprehensive monitoring framework, named ComMon, is proposed in this paper, which implements comprehensive monitoring from three aspects: network, process, and file. It has the characteristics of real-time, transparency and generality.

关键词： Monitoring Registers Kernel Linux Intrusion detection

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Anonymous Password Authentication Scheme by Using Digital Signature and Fingerprint in Cloud computing

Anonymous Password Authentication Scheme by Using Digital Si...

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International Conference on Cloud and Green computing (CGC)

作者： Ali A. Yassin Hai Jin Ayad Ibrahim Deqing Zou Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Cloud security represents a main hindrance that causes to retard its widespread adoption. Authentication considers a significance element of security in cloud environment, aiming to verify a user's identity when a user wishes to request services from cloud. There are many authentication schemes that depend on username/password, but they are considered weak techniques of cloud authentication. A more secure scheme is the two-factor authentication that does not only verify the username/password pair, but also needs a second factor such as a token device, biometric. However, the feasibility of second-factor authentication is limited by the deployment complexity, high cost and the cloud security is compromised when the token is missing or purloined. Furthermore, these schemes are failed to resist well-known attacks such as replay attacks, reflection attacks. This paper proposes two-factor authentication scheme based on Schnorr digital signature and feature extraction from fingerprint to overcome above aforementioned issues. Security analysis and experimental results illustrate that our proposed scheme can withstand the common security attacks as well, and has a good performance of password authentication.

关键词： Authentication Servers Feature extraction Cryptography Digital signatures Cloud computing

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Assessing MapReduce for Internet computing: A Comparison of Hadoop and BitDew-MapReduce

Assessing MapReduce for Internet Computing: A Comparison of ...

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International Workshop on grid computing

作者： Lu Lu Hai Jin Xuanhua Shi Gilles Fedak Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan China INRIA Universitý of Lyon Lyon France

MapReduce is emerging as an important programming model for data-intensive application. Adapting this model to desktop grid would allow taking advantage of the vast amount of computing power and distributed storage to execute new range of application able to process enormous amount of data. In 2010, we have presented the first implementation of MapReduce dedicated to Internet Desktop grid based on the BitDew middleware. In this paper, we present new optimizations to BitDew-MapReduce (BitDew-MR): aggressive task backup, intermediate result backup, task re-execution mitigation and network failure hiding. We propose a new experimental framework which emulates key fundamental aspects of Internet Desktop grid. Using the framework, we compare BitDew-MR and the open-source Hadoop middleware on grid5000. Our experimental results show that 1) BitDew-MR successfully passes all the stress-tests of the framework while Hadoop is unable to work in typical wide-area network topology which includes PC hidden behind firewall and NAT; 2) BitDew-MR outperforms Hadoop performances on several aspects: scalability, fairness, resilience to node failures, and network disconnections.

关键词： Peer to peer computing Runtime Internet Dynamic scheduling Distributed databases Heart beat Data processing

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TBF: A high-efficient query mechanism in de-duplication backup system

TBF: A high-efficient query mechanism in de-duplication back...

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7th International Conference on Advances in grid and Pervasive computing, GPC 2012

作者： Zhou, Bin Jin, Hai Xie, Xia Yuan, PingPeng Services Computing Technology and System Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China School of Computer Science and Technology South-Central University for Nationalities Wuhan China

ISBN: (纸本)9783642307669

For the big data, the fingerprints of the data chunks are very huge and cannot be stored in the memory completely. Accordingly, a new query mechanism namely Two-stage Bloom Filter mechanism is proposed. First, each bit of the second grade bloom filter represents the chunks having the identical fingerprints which reducing the rate of false positives. Second, a two-dimensional list is created corresponding to the two grade bloom filter to gather the absolute addresses of the data chunks with the identical fingerprints. Finally, we suggest a new hash function class with the strong global random characteristic. Two-stage Bloom Filter decreases the number of accessing disks, improves the speed of detecting the redundant data chunks, and reduces the rate of false positive. Our experiments indicate that Two-stage Bloom Filter reduces about 30~40% storage accessing of false positive with the same length of the first grade Bloom Filter. © 2012 Springer-Verlag.

关键词： Hash functions

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