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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Wenbin Jiang Yiming Chen Hai Jin Bin Luo Ye Chi Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781479989386

Recently, convolutional networks have achieved great successes in the field of computer vision. In order to improve the efficiency of convolutional networks, large amount of solutions focusing on training algorithms and parallelism strategies have been proposed. In this paper, a novel algorithm based on look-up table is proposed to speed up convolutional networks with small filters by applying GPU. By transforming multiplication operations in the convolution computation to some table-based summation operations, the overhead of convolution computation can be reduced largely. The process of creating table and looking up table is very appropriate for parallelization on GPU. Experiment results show that the proposed approaches can improve the speed of convolution computation by 20%-30%, compared with state-of-the-art existing works.

关键词： Convolution Graphics processing units Training Computational modeling Filtering algorithms Kernel Digital filters

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A Map-reduce Method for Training Autoencoders on Xeon Phi

A Map-reduce Method for Training Autoencoders on Xeon Phi

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IEEE International Conference on Ubiquitous computing and Communications

作者： Qiongjie Yao Xiaofei Liao Hai Jin Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781509001552

The stacked autoencoder is a deep learning model that consists of multiple autoencoders. This model has been widely applied in numerous machine learning applications. A significant amount of effort has been made to increase the size of the deep learning model with respect to the size of the training dataset and the parameter of the model to improve performance. However, training a large deep learning model is highly time consuming. Recent studies have applied the CPU cluster with thousands of machines as well as the single GPU or the GPU cluster, to train large scale deep learning models. As a high-performance coprocessor like GPU, the Xeon Phi can be an alternative tool for training large scale deep learning models on a single machine. The Xeon Phi can be recognized as a small cluster which features about 60 cores, and each core supports four hardware threads. Massive parallelism offsets the low computing capacity of every core, but challenges an efficient parallel autoencoders design. In this paper, we analyze the training algorithm of autoencoders based on the matrix operation and point out the thread oversubscription problem, which results in performance degradation. Based on the observation, we propose our map-reduce implementation of autoencoders on the Xeon Phi coprocessor. Our basic idea is to parallelize multiple autoencoder model replicas with bulk synchronous parallel (BSP) communication model where the parameters are updated after the computations of all replicas are completed. Each thread is responsible for one model replica, and all replicas work together on the same mini-batch. This data parallelism method is suitable for training autoencoders on the Xeon Phi, and can extend to asynchronous parallel training method without thread oversubscription. In our experiment the speedup is four times higher than that of sequential implementation. Enlarging the size of the autoencoder model, our method still gets stable speedup.

关键词： Deep Learning Autoencoder Xeon Phi Mapreduce BSP

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Cost-Effective Hybrid Replacement Strategy for SSD in Web Cache

Cost-Effective Hybrid Replacement Strategy for SSD in Web Ca...

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IEEE International Conference on Ubiquitous computing and Communications

作者： Qu Li Xiaofei Liao Hai Jin Li Lin Xia Xie Qiongjie Yao Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781509001552

The increasing popularity of photo sharing in social networking service (SNS) complicates the challenge of storing and transmitting large photo data for SNS providers. Distributed web caches are generally used by SNS providers to improve data transmission performance effectively. Two critical factors affect the efficiency of web caches: storage media and replacement algorithms. Solid-state drive (SSD) is often used in web caches due to its good performance. Random write performance and SSD endurance are directly affected by the characteristic of write amplification;this characteristic is also related to replacement algorithms. The least recently used (LRU) algorithm effectively computes cache hit ratio but induces severe write amplification. First in first out (FIFO) method minimizes write amplification, whereas a weak hit ratio increases bandwidth consumption. In this paper, we present a hybrid replacement strategy, namely, F-SkLRU. This strategy facilitates a good trade-off between performance and the cost of web cache. F-SkLRU takes advantage of sequential writing to reduce SSD wear. This strategy also consolidates multiple factors to improve hit ratio. Simulation results show that F-SkLRU can reduce bandwidth consumption by 14.11% to 23.4% in comparison with that of FIFO and LRU. The proposed strategy can also reduce the cost of SSD by up to 300% more than LRU can in extreme situations.

关键词： Web cache Bandwidth SSD Write amplification

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URMG: Enhanced CBMG-Based Method for Automatically Testing Web Applications in the Cloud

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Tsinghua Science and technology 2014年第1期19卷 65-75页

作者： Xiaolin Xu Hai Jin Song Wu Lixiang Tang Yihong Wang Services Computing Technology and System Lab and Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

To satisfy the rapid growth of cloud technologies, a large number of web applications have been developed and deployed, and these applications are being run in clouds. Due to the scalability provided by clouds, a single web application may be concurrently visited by several millions or billions of users. Thus, the testing and performance evaluations of these applications are increasingly important. User model based evaluations can significantly reduce the manual work required, and can enable us to determine the performance of applications under real runtime environments. Hence, it has become one of the most popular evaluation methods in both industry and academia. Significant efforts have focused on building different kinds of models using mining web access logs, such as Markov models and Customer Behavior Model Graph （CBMG）. This paper proposes a new kind of model, named the User Representation Model Graph （URMG）, which is built based on CBMG. It uses an algorithm to refine CBMG and optimizes the evaluations execution process. Based on this model, an automatic testing and evaluation system for web applications is designed, implemented, and deployed in our test cloud, which is able to execute all of the analysis and testing operations using only web access logs. In our system, the error rate caused by random access to applications in the execution phase is also reduced, and the results show that the error rate of the evaluation that depends on URMG is 50% less than that which depends on CBMG.

关键词： cloud web application performance evaluation customer behavior user representation

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Ciphertext-Policy Attribute-Based Encryption with Short Keys

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Chinese Journal of Electronics 2014年第4期23卷 655-660页

作者： XU Peng TANG Yong JIANG Wenbin JIN Hai ZOU Deqing Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

Ciphertext-policy attribute-based encryption(CP-ABE)allows a user with some attributes to decrypt the ciphertexts associated with these *** several CP-ABE schemes with the constant size ciphertext were proposed to reduce the communication cost,their master public and secret keys still have the size linear in the total number of *** schemes are unpractical for the attribute-scalable and many-attributes scenario.A new CP-ABE scheme is *** attribute is mapped to a mathematical value by a combination *** master public and secret keys of the proposed CP-ABE scheme have the size linear in the binary size of a hash function’s *** has the comparable performance with existing schemes in the aspects like the time costs of encryption and decryption,the expressiveness of access policy and the provable security.

关键词： Ciphertext-policy attribute-based encryption Expressiveness policy Provable security

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A Secured Scheme for Geographical Distance Computation over Encrypted Cloud Data

A Secured Scheme for Geographical Distance Computation over ...

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International Conference on Computer Science and Network technology

作者： Zaid Ameen Abduljabbar Hai Jin Ayad Ibrahim Aqeel Noori Mohammed Abdulridha Hussain Zaid Alaa Hussien Deqing Zou Salah H. Abbdal Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China University of Basrah Basrah Iraq

ISBN: (纸本)9781467381741

Popular service providers, such as Google and Amazon, have turned their vast resources into a cloud computing model and enforced their businesses to run applications on the servers of such new model. To ensure security and privacy in this environments, customers have to encrypt their data before uploading them into the cloud servers. Unfortunately, modern unbreakable encryption methods are inadequate because they do not have the ability to execute database queries on the encrypted data. In this paper, we address the problem of how to calculate the geographical distance over an encrypted dataset. Specifically, the data owner, Alice, sends her encrypted dataset of geographical locations into the cloud server. At any time, Bob would like to check the proximity of his submitted query from the locations of Alice. Our proposed scheme enables the untrusted server to perform such task without compromising the privacy of either the dataset of Alice or the query of Bob. Among various distance metrics, we employ the efficient principle of approximate matching to obtain the proximity between query and data locations. Furthermore, we use the inner product similarity to formalize such principle for similarity measurement. Several experiments have been conducted to investigate the overhead and the efficiency of the proposed scheme.

关键词： Cloud computing Privacy preserving Geographical distance Cloud computing Servers distance metric approximate match Dataset CLOUDS Clouds data querying similar measure Uploading Privacy cryptography Geographical Locations

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GPU-based multifrontal optimizing method in sparse Cholesky factorization

GPU-based multifrontal optimizing method in sparse Cholesky ...

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International Conference on Application Specific systems (ASAP), Architectures and Processors

作者： Ran Zheng Wei Wang Hai Jin Song Wu Yong Chen Han Jiang Services Computing Technology and System Lab University of Science and Technology Wuhan China Department of Power System China Electric Power Research Institute Beijing China

ISBN: (纸本)9781479919260

In many scientific computing applications, sparse Cholesky factorization is used to solve large sparse linear equations in distributed environment. GPU computing is a new way to solve the problem. However, sparse Cholesky factorization on GPU is hardly to achieve excellent performance due to the structure irregularity of matrix and the low GPU resource utilization. A hybrid CPU-GPU implementation of sparse Cholesky factorization is proposed based on multifrontal method. A large sparse coefficient matrix is decomposed into a series of small dense matrices (frontal matrices) in the method, and then multiple GEMM (General Matrix-matrix Multiplication) operations are computed. GEMMs are the main operations in sparse Cholesky factorization, but they are hardly to perform better in parallel on GPU. In order to improve the performance, the scheme of multiple task queues is adopted when performing multiple GEMMs parallelized with multifrontal method; all GEMM tasks are scheduled dynamically on GPU and CPU based on computation scales for load balance and computing-time reduction. Experimental results show that the approach can outperform the implementations of BLAS and cuBLAS, achieving up to 3.15× and 1.98× speedup, respectively.

关键词： Graphics processing units Sparse matrices Matrix decomposition Acceleration Instruction sets Symmetric matrices Kernel

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On performance debugging of unnecessary lock contentions on multicore processors: A replay-based approach 15

On performance debugging of unnecessary lock contentions on ...

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International Symposium on Code Generation and Optimization (CGO)

作者： Long Zheng Xiaofei Liao Bingsheng He Song Wu Hai Jin Services Computing Technology and System Lab Huazhong University of Science and Technology China School of Computer Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781479981618

Locks have been widely used as an effective synchronization mechanism among processes and threads. However, we observe that a large number of false inter-thread dependencies (i.e., unnecessary lock contentions) exist during the program execution on multicore processors, thereby incurring significant performance overhead. This paper presents a performance debugging framework, PerfPlay, to facilitate a comprehensive and in-depth understanding of the performance impact of unnecessary lock contentions. The core technique of our debugging framework is trace replay. Specifically, PerfPlay records the program execution trace, on the basis of which the unnecessary lock contentions can be identified through trace analysis. We then propose a novel technique of trace transformation to transform these identified unnecessary lock contentions in the original trace into the correct pattern as a new trace free of unnecessary lock contentions. Through replaying both traces, PerfPlay can quantify the performance impact of unnecessary lock contentions. To demonstrate the effectiveness of our debugging framework, we study five real-world programs and PARSEC benchmarks. Our experimental results demonstrate the significant performance overhead of unnecessary lock contentions, and the effectiveness of PerfPlay in identifying the performance critical unnecessary lock contentions in real applications.

关键词： Topology Synchronization Debugging Semantics Multicore processing Educational institutions Instruction sets

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Robust image document authentication code with autonomous biometrie key generation, selection, and updating in cloud environment

Robust image document authentication code with autonomous bi...

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International Symposium on Information Assurance and Security (IAS)

作者： Zaid Ameen Abduljabbar Hai Jin Zaid Alaa Hussien Ali A. Yassin Mohammed Abdulridha Hussain Salah H. Abbdal Deqing Zou Cluster and Grid Computing Lab Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China University of Basrah Basrah Iraq Southern Technical University Basrah Iraq

Recently, security issues are obstructing the development and using of cloud computing services. Authentication and integrity play an important role in the cloud security, and numerous concerns have been raised to recognize any tampering with exchanges of the image document between two entities (sender and receiver) within the cloud environment. However, none of the existing solutions reduce the probability of known attacks by combining cryptographic hash function with a strong factor that should be periodically changed. For this reason, in this paper we propose a robust one-time image document authentication scheme based on combining non-interactive onetime biometric key and a robust wavelet-based cryptographic hashing scheme. The result of the combination is one-time image document authentication code (OMAC). OMAC is hidden in an image document as a cover image through reversible data embedding steganography. The proposed scheme has several important security attributes, such as key agreement, biometric key management, robust OMAC, invulnerability, and efficiency. In biometric key management, key generation, key selection, and key update algorithms are performed autonomously by the sender and the receiver; thus, no interaction between them is needed.

关键词： Authentication Cryptography Biomedical imaging Robustness Receivers Cloud computing

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Mining user check-in features for location classification in location-based social networks

Mining user check-in features for location classification in...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Chen Yu Yang Liu Dezhong Yao Hai Jin Feng Lu Hanhua Chen Qiang Ding Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China Shannon Lab Huawei Technologies Co. LTD Beijing China

ISBN: (纸本)9781467371957

With the increasing popularity of location-based social networks, a large number of users have been involved in the check-ins. The venues where the user frequently repeats check-ins tend to play a very important role in his daily life, as they not only dominate the user's mobility behavior but also imply the user's personal preferences. Therefore, fast discerning of such check-in venues could enable us to improve a wide range of location-based services. In this paper, we propose a new location classification problem for users of location-based social networks, in which we aim to discern, given the observation that a user makes a "new" check-in at a venue, whether he will frequently repeat check-ins at this venue. To solve the problem, we first extract 16 features attached to the user's "new" check-ins. With the publicly available check-in dataset, we then train a location classifier based on Support Vector Machine and compare it with two baselines based on majority voting. The comparison results demonstrate the practicability of the trained location classifier.

关键词： Feature extraction Social network services Frequency measurement Computers Support vector machines Global Positioning system Mobile radio mobility management

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