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

作者： Jingyuan Zhao Xin Liu Yao Liu Penglong Jiao Jinshuo Liu Wei Xue School of Data Science & Engineering East China Normal University Shanghai China School of Cyber Science and Engineering Wuhan University Wuhan China National Research Centre of Parallel Computer Engineering and Technology Beijing China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Tsinghua University Beijing China

As the parallel scale of HPC applications represented by earth system models becomes larger and the computing cost becomes higher, the performance of HPC applications is increasingly critical. Profiling HPC applications accurately helps to model the applications and find the performance bottlenecks. However, due to the complexity of HPC applications, the diversity of programming languages, the differences of individual programming habits, and multiple architectures, accurate profiling becomes very tough. In this paper, we propose LPerf: a low-overhead and high-accuracy profiler for HPC applications. To reduce the profiling overhead and improve the profiling accuracy, we propose a preprocessing method which can automatically instrument with tunable granularity thus significantly reducing the run-time overhead of profiling, an aggregated caller-callee relationship which is used to locate relationship of functions efficiently, and a profiling-aware method which can precisely calculate running time of functions. The experimental results show that the error rate of profiling reaches 0.02%, and the overhead reaches 1.6%, in the earth system model named CAS-ESM. Compared with the baselines, the precision, accuracy, and overhead of LPerf have reached the state of the art.

关键词： Earth Analytical models Schedules Computer languages Costs Error analysis Computational modeling

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Optimization and Performance Modeling of Stencil Computations on ARM Architectures

Optimization and Performance Modeling of Stencil Computation...

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

作者： Kaifang Zhang Huayou Su Peng Zhang Yong Dou National Key Laboratory for Parallel and Distribution Processing National University of Defense Technology Changsha China CAEP Software Center for High Performance Numerical Simulation Beijing China Institute of Applied Physics and Computational Mathematics Beijing China

Stencil Computation has long been an omnipresent kernel of a wide range of scientific and engineering applications. There is much work investigating the stencil performance on x86 processors and accelerators such as GPU. Meanwhile, ARM processors for HPC have been highlighted more and more with Fugaku of Japan becoming the first number one system on the TOP-500 list. In this paper, we focus on modeling and optimizing the performance of stencil computation on ARM architectures. Specifically, we proposed a performance model for stencil computation based on tiling optimization to guide the optimal configuration of tiling parameters for good cache reuse. We validate the proposed model with the Performance Monitor Unit (PMU) provided by the ARM processor. Experimental results show that the prediction error of the execution time can be lower to 1.37%. Furthermore, we can achieve a maximum of 1.26 × speedup compared to the naive implementation according to the optimization parameters provided from the model.

关键词： Computational modeling High performance computing Conferences Graphics processing units Computer architecture Phasor measurement units Kernel

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Attention-based fault-tolerant approach for multi-agent reinforcement learning systems

arXiv

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arXiv 2019年

作者： Geng, Mingyang Xu, Kele Li, Yiying Liu, Shuqi Ding, Bo Wang, Huaimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory of Big Data Management and Analysis Northeastern University Shenyang110000 China

The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. In many real-world applications, the agents can only acquire a partial view of the world. However, in realistic settings, one or more agents that show arbitrarily faulty or malicious behavior may suffice to let the current coordination mechanisms fail. In this paper, we study a practical scenario considering the security issues in the presence of agents with arbitrarily faulty or malicious behavior. Under these circumstances, learning an optimal policy becomes particularly challenging, even in the unrealistic case that an agent's policy can be made conditional upon all other agents' observations. To overcome these difficulties, we present an Attention-based Fault-Tolerant (FT-Attn) algorithm which selects correct and relevant information for each agent at every time-step. The multi-head attention mechanism enables the agents to learn effective communication policies through experience concurrently to the action policies. Empirical results have shown that FT-Attn beats previous state-of-the-art methods in some complex environments and can adapt to various kinds of noisy environments without tuning the complexity of the algorithm. Furthermore, FT-Attn can effectively deal with the complex situation where an agent needs to reach multiple agents' correct observation at the same time. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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An Efficient parallel Successive Cancellation List Polar Decoder Based on GPUs

An Efficient Parallel Successive Cancellation List Polar Dec...

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

作者： Xin Zhou Rongchun Li Shijie Li Yuntao Liu Yong Dou National Key Laboratory for Parallel and Distribution Processing National University of Defense Technology Changsha China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Polar codes are a class of codes that can achieve the symmetric capacity. They are adopted to be control code for the enhanced mobile broadband (eMBB) for the fifth generation(5G) standard. Although Polar codes can be efficiently decoded by successive cancellation algorithm with complexity O(NlogN), decoding performance of this algorithm is not good enough for short codewords. The successive cancellation list(SCL) decoder is recently investigated in most studies. It has better frame error rate(FER) performance but poor latency and throughput. In this study, a parallel SCL decoder based on the graphic processing unit(GPU) is designed to reduce the latency and improve the decoding throughput. An efficient approach for sharing the intermediate values among different decoding paths is introduced. This method reduces the computing complexity and decoding latency. The implementation of parallel non-recursive decoding algorithm also increases the throughput significantly. For the typical case of code length N=1024 and list size L=4 with code rate R = 0.5, the parallel decoder based on GPU achieves throughput of 49 Mbps on Nvidia GTX 980 and 79 Mbps on Nvidia Titan X. The throughputs are 240 and 392 times higher than the decoder based on the CPU.

关键词： Decoding Polar codes Throughput Graphics processing units Measurement Complexity theory Reliability

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Versionized process based on non-volatile random-access memory for fine-grained fault tolerance

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Frontiers of Information Technology & Electronic Engineering 2018年第2期19卷 192-205页

作者： Wen-zhe ZHANG Kai LU Xiao-ping WANG Science and Technology on Parallel and Distributed Processing Laboratory College of ComputerNational University of Defense Technology

Non-volatile random-access memory（NVRAM） technology is maturing rapidly and its byte-persistence feature allows the design of new and efficient fault tolerance mechanisms. In this paper we propose the versionized process（Ver P）, a new process model based on NVRAM that is natively non-volatile and fault tolerant. We introduce an intermediate software layer that allows us to run a process directly on NVRAM and to put all the process states into NVRAM, and then propose a mechanism to versionize all the process data. Each piece of the process data is given a special version number, which increases with the modification of that piece of data. The version number can effectively help us trace the modification of any data and recover it to a consistent state after a system *** with traditional checkpoint methods, our work can achieve fine-grained fault tolerance at very little cost.

关键词： Non-volatile memory Byte-persistence Versionized process Version number

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TMDA: Task-Specific Multi-source Domain Adaptation via Clustering Embedded Adversarial Training

TMDA: Task-Specific Multi-source Domain Adaptation via Clust...

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IEEE International Conference on Data Mining (ICDM)

作者： Haotian Wang Wenjing Yang Zhipeng Lin Yue Yu State Key Laboratory of High Performance Computing (HPCL) National University of Defense Technology China National Laboratory for Parallel and Distributed Processing National University of Defense Technology China Peng Cheng Laboratory Shenzhen China

Beyond classical domain-specific adversarial training, a recently proposed task-specific framework has achieved a great success in single source domain adaptation by utilizing task-specific decision boundaries. However, compared to single-source-single-target setting, multi-source domain adaptation (MDA) shows more powerful capability to handle with most real-life cases. To align target domain with diverse multi-source domains using task-specific decision boundaries, we provide a deep insight of task-specific framework on MDA for the first time. Accordingly, we propose a novel task-specific multi-source domain adaptation method (TMDA) with a clustering embedded adversarial training process. Specifically, the proposed TMDA detects and refines less discriminative target representations through a max-min optimization over two adversarial task-specific classifiers. Moreover, our analysis implies that scattered multi-source representations disturb the adversarial training under the task-specific framework. To tight up the dispersed source representations, we embeds a relationship-based domain clustering into TMDA. Empirical results demonstrate that our TMDA outperforms state-of-the-art methods on toy dataset, sentiment analysis and digit classification.

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Quantification of transducer misalignment in ultrasound tongue imaging

arXiv

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arXiv 2020年

作者： Csapó, Tamás Gábor Xu, Kele Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Budapest Hungary MTA-ELTE Lendület Lingual Articulation Research Group Budapest Hungary National Key Lab of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China

In speech production research, different imaging modalities have been employed to obtain accurate information about the movement and shaping of the vocal tract. Ultrasound is an affordable and non-invasive imaging modality with relatively high temporal and spatial resolution to study the dynamic behavior of tongue during speech production. However, a longstanding problem for ultrasound tongue imaging is the transducer misalignment during longer data recording sessions. In this paper, we propose a simple, yet effective, misalignment quantification approach. The analysis employs MSE distance and two similarity measurement metrics to identify the relative displacement between the chin and the transducer. We visualize these measures as a function of the timestamp of the utterances. Extensive experiments are conducted on a Hungarian and Scottish English child dataset. The results suggest that large values of Mean Square Error (MSE) and small values of Structural Similarity Index (SSIM) and Complex Wavelet SSIM indicate corruptions or issues during the data recordings, which can either be caused by transducer misalignment or lack of gel. Copyright © 2020, The Authors. All rights reserved.

关键词： Ultrasonics

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MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data

MIX: A Joint Learning Framework for Detecting Both Clustered...

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IEEE International Conference on Data Mining (ICDM)

作者： Hongzuo Xu Yijie Wang Yongjun Wang Zhiyue Wu National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Mixed-type data are pervasive in real life, but very limited outlier detection methods are available for these data. Some existing methods handle mixed-type data by feature converting, whereas their performance is downgraded by information loss and noise caused by the transformation. Another kind of approaches separately evaluates outlierness in numerical and categorical features. However, they fail to adequately consider the behaviours of data objects in different feature spaces, often leading to suboptimal results. As for outlier form, both clustered outliers and scattered outliers are contained in many real-world data, but a number of outlier detectors are inherently restricted by their outlier definitions to simultaneously detect both of them. To address these issues, an unsupervised outlier detection method MIX is proposed. MIX constructs a joint learning framework to establish a cooperation mechanism to make separate outlier scoring constantly communicate and sufficiently grasp the behaviours of data objects in another feature space. Specifically, MIX iteratively performs outlier scoring in numerical and categorical space. Each outlier scoring phase can be iteratively and cooperatively enhanced by the prior knowledge given by another feature space. To target both clustered and scattered outliers, the outlier scoring phases capture the essential characteristic of outliers, i.e., evaluating outlierness via the deviation from the normal model. We show that MIX significantly outperforms eight state-of-the-art outlier detectors on twelve real-world datasets and obtains good scalability.

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Stochastic extra-gradient based alternating direction methods for graph-guided regularized minimization

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Frontiers of Information Technology & Electronic Engineering 2018年第6期19卷 755-762页

作者： Qiang LAN Lin-bo QIAO Yi-jie WANG College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

In this study, we propose and compare stochastic variants of the extra-gradient alternating direction method, named the stochastic extra-gradient alternating direction method with Lagrangian function（SEGL） and the stochastic extra-gradient alternating direction method with augmented Lagrangian function（SEGAL）, to minimize the graph-guided optimization problems, which are composited with two convex objective functions in large scale.A number of important applications in machine learning follow the graph-guided optimization formulation, such as linear regression, logistic regression, Lasso, structured extensions of Lasso, and structured regularized logistic regression. We conduct experiments on fused logistic regression and graph-guided regularized regression. Experimental results on several genres of datasets demonstrate that the proposed algorithm outperforms other competing algorithms, and SEGAL has better performance than SEGL in practical use.

关键词： Stochastic optimization Graph-guided minimization Extra-gradient method Fused logistic regression Graph-guided regularized logistic regression

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Untrusted Hardware Causes Double-Fetch Problems in the I/O Memory

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Journal of Computer Science & Technology 2018年第3期33卷 587-602页

作者： Kai Lu Peng-Fei Wang Gen Li Xu Zhou College of Computer National University of Defense Technology Changsha 410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High-Performance Computing National University of Defense Technology Changsha 410073 China

The double fetch problem occurs when the data is maliciously changed between two kernel reads of the supposedly same data, which can cause serious security problems in the kernel. Previous research focused on the double fetches between the kernel and user applications. In this paper, we present the first dedicated study of the double fetch problem between the kernel and peripheral devices （aka. the hardware double fetch）. Operating systems communicate with peripheral devices by reading from and writing to the device mapped I/O （input and output） memory. Owing to the lack of effective validation of the attached hardware, compromised hardware could flip the data between two reads of the same I/O memory address, causing a double fetch problem. We propose a static pattern-matching approach to identify the hardware double fetches from the Linux kernel. Our approach can analyze the entire kernel without relying on the corresponding hardware. The results are categorized and each category is analyzed using case studies to discuss the possibility of causing bugs. We also find four previously unknown double-fetch vulnerabilities, which have been confirmed and fixed after reporting them to the maintainers.

关键词： hardware double fetch double-fetch bug I/O memory peripheral device double-fetch vulnerability

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