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CWLP:coordinated warp scheduling and locality-protected cache allocation on GPUs

Frontiers of Information technology & Electronic Engineering 2018年第2期19卷 206-220页

作者： Yang ZHANG Zuo-cheng XING Cang LIU Chuan TANG National Laboratory for Parallel and Distributed Processing National University of Defense Technology

As we approach the exascale era in supercomputing, designing a balanced computer system with a powerful computing ability and low power requirements has becoming increasingly important. The graphics processing unit（GPU） is an accelerator used widely in most of recent supercomputers. It adopts a large number of threads to hide a long latency with a high energy efficiency. In contrast to their powerful computing ability, GPUs have only a few megabytes of fast on-chip memory storage per streaming multiprocessor（SM）. The GPU cache is inefficient due to a mismatch between the throughput-oriented execution model and cache hierarchy design. At the same time, current GPUs fail to handle burst-mode long-access latency due to GPU＇s poor warp scheduling ***, benefits of GPU＇s high computing ability are reduced dramatically by the poor cache management and warp scheduling methods, which limit the system performance and energy efficiency. In this paper, we put forward a coordinated warp scheduling and locality-protected（CWLP） cache allocation scheme to make full use of data locality and hide latency. We first present a locality-protected cache allocation method based on the instruction program counter（LPC） to promote cache performance. Specifically, we use a PC-based locality detector to collect the reuse information of each cache line and employ a prioritised cache allocation unit（PCAU） which coordinates the data reuse information with the time-stamp information to evict the lines with the least reuse possibility. Moreover, the locality information is used by the warp scheduler to create an intelligent warp reordering scheme to capture locality and hide latency. Simulation results show that CWLP provides a speedup up to 19.8% and an average improvement of 8.8% over the baseline methods.

关键词： Locality Graphics processing unit （GPU） Cache allocation Warp scheduling

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VISUAL CONFUSION LABEL TREE FOR IMAGE CLASSIFICATION

arXiv

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arXiv 2019年

作者： Liu, Yuntao Dou, Yong Jin, Ruochun Li, Rongchun National University of Defense Technology National Laboratory for Parallel and Distributed Processing Changsha410073 China

Convolution neural network models are widely used in image classification tasks. However, the running time of such models is so long that it is not the conforming to the strict real-time requirement of mobile devices. In order to optimize models and meet the requirement mentioned above, we propose a method that replaces the fully-connected layers of convolution neural network models with a tree classifier. Specifically, we construct a Visual Confusion Label Tree based on the output of the convolution neural network models, and use a multi-kernel SVM plus classifier with hierarchical constraints to train the tree classifier. Focusing on those confusion subsets instead of the entire set of categories makes the tree classifier more discriminative and the replacement of the fully-connected layers reduces the original running time. Experiments show that our tree classifier obtains a significant improvement over the state-of-the-art tree classifier by 4.3% and 2.4% in terms of top-1 accuracy on CIFAR-100 and ImageNet datasets respectively. Additionally, our method achieves 124× and 115× speedup ratio compared with fully-connected layers on AlexNet and VGG16 without accuracy decline. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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Approximate Iteration Detection and Precoding in Massive MIMO

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China Communications 2018年第5期15卷 183-196页

作者： Chuan Tang Yerong Tao Yancang Chen Cang Liu Luechao Yuan Zuocheng Xing Luoyang Electronic Equipment Test Center LuoYang 471000China National Laboratory for Parallel and Distributed Processing National University of Defense TechnologyChangsha 410073China

Massive multiple-input multiple-output provides improved energy efficiency and spectral efficiency in 5 G. However it requires large-scale matrix computation with tremendous complexity, especially for data detection and precoding. Recently, many detection and precoding methods were proposed using approximate iteration methods, which meet the demand of precision with low complexity. In this paper, we compare these approximate iteration methods in precision and complexity, and then improve these methods with iteration refinement at the cost of little complexity and no extra hardware resource. By derivation, our proposal is a combination of three approximate iteration methods in essence and provides remarkable precision improvement on desired vectors. The results show that our proposal provides 27%-83% normalized mean-squared error improvement of the detection symbol vector and precoding symbol vector. Moreover, we find the bit-error rate is mainly controlled by soft-input soft-output Viterbi decoding when using approximate iteration methods. Further, only considering the effect on soft-input soft-output Viterbi decoding, the simulation results show that using a rough estimation for the filter matrix of minimum mean square error detection to calculating log-likelihood ratio could provideenough good bit-error rate performance, especially when the ratio of base station antennas number and the users number is not too large.

关键词： massive MIMO detection and precoding matrix inversion iteration refinement soft Viterbi decoding

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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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Visual Tree Convolutional Neural Network in Image Classification

arXiv

引用

arXiv 2019年

作者： Liu, Yuntao Dou, Yong Jin, Ruochun Qiao, Peng National University of Defense Technology National Laboratory for Parallel and Distributed Processing Hunan Changsha410073 China

In image classification, Convolutional Neural Network(CNN) models have achieved high performance with the rapid development in deep learning. However, some categories in the image datasets are more difficult to distinguished than others. Improving the classification accuracy on these confused categories is benefit to the overall performance. In this paper, we build a Confusion Visual Tree(CVT) based on the confused semantic level information to identify the confused categories. With the information provided by the CVT, we can lead the CNN training procedure to pay more attention on these confused categories. Therefore, we propose Visual Tree Convolutional Neural Networks(VT-CNN) based on the original deep CNN embedded with our CVT. We evaluate our VT-CNN model on the benchmark datasets CIFAR-10 and CIFAR-100. In our experiments, we build up 3 different VT-CNN models and they obtain improvement over their based CNN models by 1.36%, 0.89% and 0.64%, respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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Profiling HPC Applications with Low Overhead and High Accuracy

Profiling HPC Applications with Low Overhead and High Accura...

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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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ECDT: Exploiting Correlation Diversity for Knowledge Transfer in Partial Domain Adaptation

ECDT: Exploiting Correlation Diversity for Knowledge Transfe...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Shichang He Xuan Liu Xinning Chen Ying Huang College of Computer Science and Electronic Engineering Hunan University P.R. China The Science and Technology on Paraller and Distributed Processing Laboratory Tsinghua University P.R. China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Domain adaptation aims to transfer knowledge across different domains and bridge the gap between them. While traditional knowledge transfer considers identical domain, a more realistic scenario is to transfer from a larger and more diverse source domain to a smaller target domain, which is referred to as partial domain adaptation (PDA). However, matching the whole source domain to the target domain for PDA might produce negative transfer. Samples in the shared classes should be carefully selected to mitigate negative transfer in PDA. We observe that the correlations between different target domain samples and source domain samples are diverse: classes are not equally correlated and moreover, different samples have different correlation strengthes even when they are in the same class. In this study, we propose ECDT, a novel PDA method that Exploits the Correlation Diversity for knowledge Transfer between different domains. We propose a novel method to estimate target domain label space that utilizes the label distribution and feature distribution of target samples, based on which outlier source classes can be filtered out and their negative effects on transfer can be mitigated. Moreover, ECDT combines class-level correlation and instance-level correlation to quantity sample-level transferability in domain adversarial network. Experimental results on three commonly used cross-domain object data sets show that ECDT is superior to previous partial domain adaptation methods.

关键词： Correlation Handheld computers Tagging Sensors Task analysis Knowledge transfer

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Succinct Representations in Collaborative Filtering: A Case Study using Wavelet Tree on 1,000 Cores

Succinct Representations in Collaborative Filtering: A Case ...

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IEEE International Conference on parallel and distributed Computing, Applications and Technologies (PDCAT)

作者： Xiangjun Peng Qingfeng Wang Xu Sun Chunye Gong Yaohua Wang User-Centric Computing Group University of Nottingham Ningbo China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

User-Item (U-I) matrix has been used as the dominant data infrastructure of Collaborative Filtering (CF). To reduce space consumption in runtime and storage, caused by data sparsity and growing need to accommodate side information in CF design, one needs to go beyond the U-I Matrix. In this paper, we took a case study of Succinct Representations in Collaborative Filtering, rather than using a U-I Matrix. Our key insight is to introduce Succinct Data Structures as a new infrastructure of CF. Towards this, we implemented a User-based K-Nearest-Neighbor CF prototype via Wavelet Tree, by first designing a Accessible Compressed Documents (ACD) to compress U-I data in Wavelet Tree, which is efficient in both storage and runtime. Then, we showed that ACD can be applied to develop an efficient intersection algorithm without decompression, by taking advantage of ACD's characteristics. We evaluated our design on 1,000 cores of Tianhe-II supercomputer, with one of the largest public data set ml-20m. The results showed that our prototype could achieve 3.7 minutes on average to deliver the results.

关键词： Vegetation Collaboration Filtering Data structures Runtime Indexes Time complexity

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Exploring frame segmentation networks for temporal action localization

arXiv

引用

arXiv 2019年

作者： Yang, Ke Shen, Xiaolong Qiao, Peng Li, Shijie Li, Dongsheng Dou, Yong National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

Temporal action localization is an important task of computer vision. Though many methods have been proposed, it still remains an open question how to predict the temporal location of action segments precisely. Most state-of-the-art works train action classifiers on video segments pre-determined by action proposal. However, recent work found that a desirable model should move beyond segment-level and make dense predictions at a fine granularity in time to determine precise temporal boundaries. In this paper, we propose a Frame Segmentation Network (FSN) that places a temporal CNN on top of the 2D spatial CNNs. Spatial CNNs are responsible for abstracting semantics in spatial dimension while temporal CNN is responsible for introducing temporal context information and performing dense predictions. The proposed FSN can make dense predictions at frame-level for a video clip using both spatial and temporal context information. FSN is trained in an end-to-end manner, so the model can be optimized in spatial and temporal domain jointly. We also adapt FSN to use it in weakly supervised scenario (WFSN), where only video level labels are provided when training. Experiment results on public dataset show that FSN achieves superior performance in both frame-level action localization and temporal action localization. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Container-Based Complex Programming Skills Training Platform

Container-Based Complex Programming Skills Training Platform

引用

IEEE International Conference on Software Engineering and Service sciences (ICSESS)

作者： Wei Wang Tao Wang Gang Yin National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha Hunan Province China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

In recent years, online programming education and evaluation have developed rapidly. The traditional online education platform focuses on the teaching of knowledge, lacking a good evaluation platform to assess the learning outcomes, while the existing online evaluation platform only focuses on the evaluation of algorithms or languages. Until now, no platform can support the training of engineering-level complex technologies. In this paper, we present our solution - Educoder. Educoder is an one-stop service platform that integrates “teaching”, “learning” and “use”. It not only supports simple programming language training type, but also supports the training of complex technology such as Hadoop, Spring, Machine Learning, Docker, etc. The code environment for student learning and evaluation is managed by Gitlab, and the runtime environment is provided by Kubernetes pod, which guarantees good isolation. For complex types of training, we provide the Docker Web Terminal module for SSH connection to enter the pod for operation and use docker in docker technology to provide support for training that requires multiple services or multiple nodes. In Educoder, we use scripts to serialize the training evaluation steps, and we formed a private evaluation script template library for training teacher to reference, which greatly reduced the time cost of writing a new training course.

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