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13th IEEE International Workshop on FTDCS 2011, the 8th International Conference on ATC 2011, the 8th International Conference on UIC 2011 and the 13th IEEE International Conference on HPCC 2011

作者： Zhang, Dong-Song Chen, Fang-Yuan Li, Hong-Hua Jin, Shi-Yao Guo, De-Ke National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China School of Computing Science Simon Fraser University 8888 University Drive Burnaby BC V5A 1S6 Canada National Laboratory for Information System Engineering School of Information Systems and Management National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9780769545387

As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, the real-time energy-efficient scheduling in multiprocessor systems becomes an urgent problem to reduce the system energy consumption while meeting real-time constraints. For a multiprocessor with independent DVFS and DPM at each processor, this paper proposes an energy-efficient real-time scheduling algorithm named LRE-DVFS-EACH, based on LRE-TL which is an optimal real-time scheduling algorithm for sporadic tasks. LRE-DVFS-EACH utilizes the concept of TL plane and the idea of fluid scheduling to dynamically scale the voltage and frequency of processors at the initial time of each TL plane as well as the release time of a sporadic task in each TL plane. Consequently, LRE-DVFS-EACH can obtain a reasonable tradeoff between the real-time constraints and the energy saving. LRE-DVFS-EACH is also adaptive to the change of workload caused by the dynamic release of sporadic tasks, which can obtain more energy savings. The experimental results show that compared with existing algorithms, LRE-DVFS-EACH can not only guarantee the optimal feasibility of sporadic tasks, but also achieve more energy savings in all cases, especially in the case of high workloads. © 2011 IEEE.

关键词： Real time systems

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Memory Access Analysis of Many-core System with Abundant Bandwidth

Memory Access Analysis of Many-core System with Abundant Ban...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Chuan Tang Dan Liu Zuocheng Xing Peng Yang Zhe Wang Jiang Xu Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)9781479986712

Many-core system is main architecture trend currently. One of the dominating challenges for on-chip manycore system is the memory wall. However traditional research primarily focus on the limited bandwidth. To solve this problem, many-core system is aided with large cache, and a lot of complex approaches about memory and cache are adopted aiming at relaxing the pressure of bandwidth and improving the efficiency of cache. All these methods generate much cost of area and power. In this paper, we are motivated by the feature of abundant bandwidth and low latency of optical interconnect. We analyze the characteristics of memory access on 64 cores system under the case of high bandwidth which can be assumed to benefit from optical interconnect, considering the sensibility with bandwidth and cache for different benchmarks. Finally, we discuss about promising basic frameworks suitable for manycore system with optical interconnect.

关键词： Bandwidth Benchmark testing Optical interconnections Random access memory Delays Hidden Markov models Computers

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Optimizing Reference Gene Set for Inter-Chip Normalization Using Genetic Algorithm

Optimizing Reference Gene Set for Inter-Chip Normalization U...

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Fei Li Wenjian Xu Peng Li XiaoChen Bo Yuxing Peng Department of Computer Science National Laboratory of Parallel & Distributed Processing National University of Defense Technology Changsha China Beijing Institute of Radiation Medicine Beijing China

Due to the existence of systematic bias in the measurements, inter-chip normalization is required before integrated data analysis of multiple microarray datasets. Current inter-chip normalization methods rely on some assumptions, which can rarely be verified by experiments, such as the invariant expression level of housekeeping genes or the stability of statistical data distribution. In this paper, we present a highly verifiable inter-chip normalization method using genetic optimization algorithm, which depends only on a small number of housekeeping genes, and therefore facilitates experimental validation without any accuracy lost.

关键词： Genetic algorithms Gene expression Laboratories Data analysis Optimization methods Genomics Bioinformatics Statistical distributions Probes Computer science

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Learning generic diffusion processes for image restoration

arXiv

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arXiv 2018年

作者： Qiao, Peng Dou, Yong Chen, Yunjin Feng, Wensen Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China ULSee Inc. Hangzhou China College of Computer Science & Software Engineering Shenzhen University Shenzhen China

Image restoration problems are typical ill-posed problems where the regularization term plays an important role. The regularization term learned via generative approaches is easy to transfer to various image restoration, but offers inferior restoration quality compared with that learned via discriminative approaches. On the contrary, the regularization term learned via discriminative approaches are usually trained for a specific image restoration problem, and fail in the problem for which it is not trained. To address this issue, we propose a generic diffusion process (genericDP) to handle multiple Gaussian denoising problems based on the Trainable Non-linear Reaction Diffusion (TNRD) models. Instead of one model, which consists of a diffusion and a reaction term, for one Gaussian denoising problem in TNRD, we enforce multiple TNRD models to share one diffusion term. The trained genericDP model can provide both promising denoising performance and high training efficiency compared with the original TNRD models. We also transfer the trained diffusion term to non-blind deconvolution which is unseen in the training phase. Experiment results show that the trained diffusion term for multiple Gaussian denoising can be transferred to image non-blind deconvolution as an image prior and provide competitive performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Diffusion

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An Irregular cutting based distributed mapping algorithm for DVE systems

An Irregular cutting based distributed mapping algorithm for...

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Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA

作者： Wei Zhang Hangjun Zhou Yuxing Peng Sikun Li Min Xi National Laboratory of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China Department of Computer Science Xi'an Jiaotong University Xi'an China

distributed Virtual Environment (DVE) systems have become more and more important both in academic communities and the industries. To guarantee the load constrain, the physical world integrity and the virtual world integrity, the virtual environment should be divided into several parts and mapped to multiple servers, which are located in geographically different places. Many centralized algorithms based on regular cutting method are proposed to solve this problem. However, the dynamic property of clients and distributed property of the system make the existing algorithms unable to undertake it. In this paper, we propose a metric to measure the cutting quality and design a distributed mapping algorithm based on an irregular cutting. Simulation results show that our algorithm greatly decreases the costs of DVE systems for migration of clients and effectively improves the satisfaction degree of users.

关键词： Defense industry Virtual environment Computer industry Computer science Algorithm design and analysis Delay Partitioning algorithms Industrial training Computational intelligence Application software

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Quantization improvements for LU Decomposition to Solve Linear Equations *

Quantization improvements for LU Decomposition to Solve Line...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Lin Chen Kang He Hao Jiang Roberto Barrio Jie Liu Tiejun Li Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China University of Zaragoza Zaragoza Spain

Quantization is an effective method for compressing Deep Neural Networks. Now, it is considered to accelerate the traditional HPC applications. In this article, we present a quantization method (q8gemm) using integer 8-bits(INT8) for matrix multiplication, which is implemented by block techniques to overcome the overflow problem. We choose different scales for one matrix’s each row and those for the other matrix’s each column to keep the numbers in range. With the intel advanced matrix extension cblas_gemm_s8u8s32, our q8gemm can accelerate panel-matrix multiplication in LU factorization. Furthermore, we propose a new mixed-precision solver (q8gesv 1 ) for the linear equations. Using q8gemm in INT8, q8gesv modifies LU factorization in FP32 and then utilizes GMRES iterative refinement in FP64 to bring the solution back to 64 bits accuracy. Numerical tests show that on two Intel CPU platforms with avx2 and avx512, q8gemm achieves a speedup of 1.313-1.621 over cblas_sgemm of MKL. The execution time of cblas_gemm_s8u8s32 makes up 86.78%-90.85% of the q8gemm. By means of q8gemm, our q8gesv can achieve a speedup of 1.176-1.493 over the optimized Higham’s mixed-precision algorithm utilized in the HPL-AI benchmark and a speedup of 1.1-1.459 over the LAPACKE_dsgesv function from MKL LAPACK. Meanwhile, q8gesv runs up to 2.859X faster on Intel(R) Core(TM) i5-10600KF and to 2.224X faster on Intel(R) Xeon(R) Gold 6130 than the LAPACKE_dgesv function from MKL LAPACK.

关键词： Deep learning Gold Quantization (signal) Benchmark testing

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HybridSwap: A scalable and synthetic framework for guest swapping on virtualization platform

HybridSwap: A scalable and synthetic framework for guest swa...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Pengfei Zhang Xi Li Rui Chu Huaimin Wang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China School of Information Science and Engineering Central South University Changsha China

In IaaS cloud environments, peak memory demand caused by hotspot applications in Virtual Machine (VM) often results in performance degradation within and outside of this VM. Some solutions such as host swapping and ballooning for memory consolidation and overcommitment have been proposed. These solutions, however, have no help for addressing guest swapping issues inside VM. Even though host holds sufficient memory pages, guest OS is unable to utilize free pages in host directly due to the semantic gap between VMM and it. Our goal is to alleviate the performance degradation by decreasing disk I/O operations generated by guest swapping. Based on the insight analysis of behavioral features of guest swapping, we design HybridSwap, a distributed scalable framework which organize surplus memory in all hosts within data center into virtual pools for swapping. This framework builds up a synthetic swapping mechanism in a peer-to-peer way, which VM can adaptively choose suitable pools for swapping. We implement the prototype of HybridSwap and evaluate it with different benchmarks. The results demonstrate that our solution has the ability to promote the guest swapping efficiency indeed. Even in some cases, it shows 2-5 times of performance promotion compared with the baseline setup.

关键词： Benchmark testing Virtualization Semantics Servers Operating systems Degradation Instruction sets

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Sample dropout for audio scene classification using multi-scale dense connected convolutional neural network

arXiv

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arXiv 2018年

作者： Feng, Dawei Xu, Kele Mi, Haibo Liao, Feifan Zhou, Yan Science and Technology on Parallel and Distributed Laboratory School of Computer National University of Defense Technology Changsha410073 China School of Information and Communication National University of Defense Technology Wuhan430010 China

Acoustic scene classification is an intricate problem for a machine. As an emerging field of research, deep Convolutional Neural Networks (CNN) achieve convincing results. In this paper, we explore the use of multi-scale Dense connected convolutional neural network (DenseNet) for the classification task, with the goal to improve the clas-sification performance as multi-scale features can be extracted from the time-frequency representation of the audio signal. On the other hand, most of previous CNN-based audio scene classification approaches aim to improve the classification accuracy, by employing different regularization techniques, such as the dropout of hidden units and data augmentation, to reduce overfitting. It is widely known that outliers in the training set have a high negative inuence on the trained model, and culling the outliers may improve the classification performance, while it is often under-explored in previous studies. In this paper, inspired by the silence removal in the speech signal processing, a novel sample dropout approach is proposed, which aims to remove outliers in the training dataset. Using the DCASE 2017 audio scene classification datasets, the experimental results demonstrates the proposed multi-scale DenseNet providing a su-perior performance than the traditional single-scale DenseNet, while the sample dropout method can further improve the classification robustness of multi-scale DenseNet. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistics

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Enhancing Temporal Alignment with Autoencoder Regularization

Enhancing Temporal Alignment with Autoencoder Regularization

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International Joint Conference on Neural Networks

作者： Liquan Nie Yuanyuan Wang Xiang Zhang Xuhui Huang Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Department of Basic Courses Army Officer Academy Department of Computer Science and Technology College of Computer National University of Defense Technology

ISBN: (纸本)9781509006212

Temporal alignment aligns two temporal sequences and is quite challenging due to drastic differences among temporal sequences and source data from different views. Canonical time warping (CTW) has shown great potential in temporal alignment tasks because it can reduce data redundancy by transforming high-dimensional data to a lower-dimensional subspace via canonical correlation analysis (CCA). However, CTW cannot uncover the underlying nonlinear structure embedded in the dataset. In this paper, we propose an autoencoder regularized canonical time warping method (AECTW) to overcome this drawback. Specifically, AECTW enhances lower-dimensional representation of each sequence by incorporating an autoencoder regularization, meanwhile reveals the nonlinear structure of features by explicit nonlinear transformation. By these strategies, AECTW significantly boosts CTW in temporal alignment tasks. Experiments on both synthetic data and two practical human action datasets demonstrate that AECTW outperforms the representative DTW-based methods.

关键词： NONLINEAR STRUCTURES data redundancy Dataset synthetic data Alignment Warping Structural properties Canonical Religious Missions Data sources

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Maximizing Uniform Multicast Throughput in Multi-Channel Dense Wireless Sensor Networks 12

Maximizing Uniform Multicast Throughput in Multi-Channel Den...

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12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Jiao, Xianlong Chen, Guirong Wang, Xiaodong Chen, Yuli Yang, Li Information and Navigation College Air Force Engineering University Xi'an710077 China College of Information System and Management National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Chongqing Guanyinqiao Elementary School Chongqing400020 China Chongqing Liangjiangxinqu Renhe Experimental School Chongqing400021 China

ISBN: (纸本)9781509056965

This paper investigates the problem of maximizing uniform multicast throughput (MUMT) for multi-channel dense wireless sensor networks, where all nodes locate within one-hop transmission range and can communicate with each other on multiple orthogonal channels. This kind of networks show wide application in the real world, and maximizing uniform multicast throughput for these networks is worth deep studying. Previous researches have proved MUMT problem is NP-hard. However, previous researches are either hard to implement, or use too many relay nodes to complete the multicast task, and thus incur high overhead or poor performance. To efficiently solve MUMT problem, we adopt the concept of the maximum independent set with the size constraint, and present one novel Single-Broadcast based Multicast algorithm called SBM based on the concept. We prove that SBM algorithm achieves a constant ratio to the theoretical throughput upper bound. Extensive experimental results demonstrate that, SBM performs better than existing work in terms of both the uniform multicast throughput and the total number of transmissions. © 2016 IEEE.

关键词： Throughput

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