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

作者： Penghui Ma Peichang Shi Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.

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Surrogate Supervision-based Deep Weakly-supervised Anomaly Detection

Surrogate Supervision-based Deep Weakly-supervised Anomaly D...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

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

ISBN: (纸本)9781665424288

Many anomaly detection applications can provide partially observed anomalies, but only limited work is for this setting. Additionally, a number of anomaly detectors focus on learning a particular model of normal/abnormal class. However, the intra-class model might be too complicated to be accurately learned. It is still a non-trivial task to handle data with anomalies/inliers in skewed and heterogeneous distributions. To address these problems, this paper proposes an anomaly detection method to leverage Partially Labeled anomalies via Surrogate supervision-based Deviation learning (denominated PLSD). The original supervision (i.e., known anomalies and a set of explored inliers) is transferred to semantic-rich surrogate supervision signals (i.e., anomaly-inlier and inlier-inlier class) via vector concatenation. Then different relationships and interactions between anomalies and inliers are directly and efficiently learned thanks to the neural network’s connection property. Anomaly scoring is processed via the trained network and the high-efficacy inliers. Extensive experiments show that PLSD significantly prevails state-of-the-art semi/weakly-supervised anomaly detectors.

关键词： Learning systems Conferences Detectors Gaussian distribution Benchmark testing Task analysis Anomaly detection

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Word Embedding-based Context-sensitive Network Flow Payload Anomaly Detection

Word Embedding-based Context-sensitive Network Flow Payload ...

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International Conference on Applied Machine Learning (ICAML)

作者： Yizhou Li Yijie Wang Li Cheng Hongzuo Xu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665421263

Payload anomaly detection can discover malicious behaviors hidden in network packets. It is hard to handle payload due to its various possible characters and complex semantic context, and thus identifying abnormal payload is also a non-trivial task. Prior art only uses the n-gram language model to extract features, which directly leads to ultra-high-dimensional feature space and also fails to capture the context semantics fully. Accordingly, this paper proposes a word embedding-based context-sensitive network flow payload anomaly detection method (termed WECAD). First, WECAD obtains the initial feature representation of the payload through the word embedding-based method. Then, we propose a corpus pruning algorithm, which applies the cosine similarity clustering and frequency distribution to prune inconsequential characters. We only keep the essential characters to reduce the calculation space. Subsequently, we propose a context learning algorithm. It employs the co-occurrence matrix transformation technology and introduces the backward step size to consider the order relationship of essential characters. Comprehensive experiments on real-world intrusion detection datasets validate the effectiveness of our method.

关键词： Art Semantics Clustering algorithms Intrusion detection Machine learning Feature extraction Task analysis

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A Memory Saving Mechanism Based on Data Transferring for Pipeline Parallelism

A Memory Saving Mechanism Based on Data Transferring for Pip...

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

作者： Wei Jiang Rui Xu Sheng Ma Qiong Wang Xiang Hou Hongyi Lu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

The correctness and robustness of the neural network model are usually proportional to its depth and width. Currently, the neural network models become deeper and wider to cope with complex applications, which leads to high memory capacity requirement and computer capacity requirements of the training process. The multi-accelerator parallelism is a promising choice for the two challenges, which deploys multiple accelerators in parallel for training neural networks. Among them, the pipeline parallel scheme has a great advantage in training speed, but its memory capacity requirements are relatively higher than other parallel schemes. Aiming at solving this challenge of pipeline parallel scheme, we propose a data transfer mechanism, which effectively reduces the peak memory usage of the training process by real-time data transferring. In the experiment, we implement our design and apply it to Pipedream, a mature pipeline parallel scheme. The memory requirement of training process is reduced by up to 48.5%, and the speed loss is kept within a reasonable range.

关键词： Training Computational modeling Pipelines Neural networks Memory management Bandwidth Parallel processing

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Fden: Mining Effective Information of Features in Detecting Network Anomalies

Fden: Mining Effective Information of Features in Detecting ...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Bin Li Yijie Wang Mingyu Liu Kele Xu Zhongyang Wang Li Cheng Yizhou Li Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

Network anomaly detection is important for detecting and reacting to the presence of network attacks. In this paper, we propose a novel method to effectively leverage the features in detecting network anomalies, named FDEn, consisting of flow-based Feature Derivation (FD) and prior knowledge incorporated Ensemble models (En pk). To mine the effective information in features, 149 features are derived to enrich the feature set of the original data with covering more characteristics of network traffic. To leverage these features effectively, an ensemble model En pk, including CatBoost and XGBoost, based on the bagging strategy is proposed to first detect anomalies by combining numerical features and categorical features. And then, En pk adjusts the predicted label of specific data by incorporating the prior knowledge of network security. We conduct empirically experiments on the data set provided by the Network Anomaly Detection Challenge (NADC), in which we obtain average improvement up to 61.6%, 31.7%, 50.2%, and 45.0%, in terms of the cost score, precision, recall and F1-score, respectively.

关键词： Knowledge engineering Conferences Telecommunication traffic Signal processing Feature extraction Numerical models Security

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Co-designing the Topology/Algorithm to Accelerate distributed Training

Co-designing the Topology/Algorithm to Accelerate Distribute...

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

作者： Xiang Hou Rui Xu Sheng Ma Qiong Wang Wei Jiang Hongyi Lu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

With the development of Deep Learning (DL), Deep Neural Network (DNN) models have become more complex. At the same time, the development of the Internet makes it easy to obtain large data sets for DL training. Large-scale model parameters and training data enhance the level of AI by improving the accuracy of DNN models. But on the other hand, they also present more severe challenges to the hardware training platform because training a large model needs a lot of computing and memory resources that can easily exceed the capacity of a single processor. In this context, integrating more processors on a hierarchical system to conduct distributed training is a direction for the development of training platforms. In distributed training, collective communication operations (including all-to-all, all-reduce, and all-gather) take up a lot of training time, making the interconnection network between computing nodes one of the most critical factors affecting the system performance. The hierarchical torus topology, combined with the Ring All-Reduce collective communication algorithm, is one of the current mainstream distributed interconnection networks. However, we believe that its communication performance is not the best. In this work, we first designed a new intra-package communication topology, i.e. the switch-based fully connected topology, which shortens the time consumed by cross-node communication. Then, considering the characteristics of this topology, we carefully devised more efficient all-reduce and all-gather communication algorithms. Finally, combined with the torus topology, we implemented a novel distributed DL training platform. Compared with the hierarchical torus, our platform improves communication efficiency and provides 1.16-2.68 times speedup in distributed training of DNN models.

关键词： Training Deep learning Network topology Multiprocessor interconnection Computational modeling Training data Switches

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Deep Isolation Forest for Anomaly Detection

arXiv

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arXiv 2022年

作者： Xu, Hongzuo Pang, Guansong Wang, Yijie Wang, Yongjun The College of Computer National University of Defense Technology Changsha410073 China The Science and Technology on Parallel and Distributed Processing Laboratory China The School of Computing and Information Systems Singapore Management University 178902 Singapore

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard anomalies that are difficult to isolate in high-dimensional/non-linear-separable data space, and (ii) notorious algorithmic bias that assigns unexpectedly lower anomaly scores to artefact regions. These issues contribute to high false negative errors. Several iForest extensions are introduced, but they essentially still employ shallow, linear data partition, restricting their power in isolating true anomalies. Therefore, this paper proposes deep isolation forest. We introduce a new representation scheme that utilises casually initialised neural networks to map original data into random representation ensembles, where random axis-parallel cuts are subsequently applied to perform the data partition. This representation scheme facilitates high freedom of the partition in the original data space (equivalent to non-linear partition on subspaces of varying sizes), encouraging a unique synergy between random representations and random partition-based isolation. Extensive experiments show that our model achieves significant improvement over state-of-the-art isolation-based methods and deep detectors on tabular, graph and time series datasets;our model also inherits desired scalability from iForest. © 2022, CC BY.

关键词： Anomaly detection

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A Complexity-Reduced Block-Selective Algebraic Multigrid Method for Implicitly Coupled Velocity-Pressure System on High Performance Computers

SSRN

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SSRN 2023年

作者： Liang, Yuechao Guo, Xiao-Wei Li, Chao Yuan, Fan Song, Min Zhang, Qingyang Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Mathematics and Computational Sciences Xiangtan University Xiangtan411105 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

The implicitly coupled pressure-based algorithm is widely acknowledged for its superior convergence and robustness in solving incompressible flow problems. However, the increased expansion scale of equations and difficulty in solving the resulting equation system has limited the widespread use of the coupling algorithm. This paper presents an optimized block-SAMG method, referred to as Red-SAMG, to significantly reduce computational complexity and memory consumption. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. An aggressive coarsening strategy further reduces complexity to address larger-scale problems. We conducted numerical experiments on a serial two-dimensional backward-facing step case and a parallel three-dimensional tunnel between rod bundles in the reactor core problem. The results demonstrate that the Red-SAMG method with aggressive coarsening reduces solution time by 12\% on the backward-facing step case. For large-scale three-dimensional problems under the same parallel conditions, the Red-SAMG method shortens the solution time by 14\% compared to the existing SAMG method. The Red-SAMG method with aggressive coarsening further reduces solution time by 49\%. Our optimized Red-SAMG method effectively reduces the complexity of the linear system arising from the implicitly coupled algorithm. The aggressive coarsening strategy also represents a significant advancement in improving the computational efficiency of the SAMG method. © 2023, The Authors. All rights reserved.

关键词： Coarsening

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OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms

arXiv

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arXiv 2022年

作者： Zhuang, Jia-Xin Huang, Xiansong Yang, Yang Chen, Jiancong Yu, Yue Gao, Wei Li, Ge Chen, Jie Zhang, Tong Peng Cheng Laboratory Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Electronic and Computer Engineering Peking University China National Laboratory for Parallel and Distributed Processing National University of Defense Technology China

In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms. Various medical image analysis methods, including 2D/3D medical image classification, segmentation, localisation, and detection, have been included in the toolbox with PyTorch and/or MindSpore implementations under heterogeneous NVIDIA and Huawei Ascend computing systems. To our best knowledge, OpenMedIA is the first open-source algorithm library providing compared PyTorch and MindSpore implementations and results on several benchmark datasets. The source codes and models are available at https://***/ OpenMedIA. © 2022, CC BY-NC-SA.

关键词： Open systems

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Neutronic calculations of the China dual-functional lithium–lead test blanket module with the parallel discrete ordinates code Hydra

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Nuclear science and Techniques 2020年第8期31卷 1-12页

作者： Guang-Chun Zhang Jie Liu Liang-Zhi Cao Hong-Chun Wu Xian-Bao Yuan Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China School of Nuclear Science and Technology Xi’an Jiaotong UniversityXi’an 710049China College of Mechanical and Power Engineering China Three Gorges UniversityYichang 443002China

The China dual-functional lithium–lead test blanket module(DFLL-TBM) is a liquid Li Pb blanket concept developed by the Institute of Nuclear Energy Safety technology of the Chinese Academy of sciences for testing in ITER to validate relevant tritium breeding and shielding technologies. In this study, neutronic calculations of DFLL-TBM were carried out using a massively parallel three-dimensional transport code, Hydra, with the Fusion Evaluated Nuclear Data Library/MG. Hydra was developed by the Nuclear Engineering Computational Physics Lab based on the discrete ordinates method and has been devoted to neutronic analysis and shielding evaluation for nuclear facilities. An in-house Monte Carlo code(MCX) was employed to verify the discretized calculation model used by Hydra for the DFLL-TBM calculations. The results showed two key aspects:(1) In most material zones,Hydra solutions are in good agreement with the reference MCX results within 1%, and the maximal relative difference of the neutron flux is merely 3%, demonstrating the correctness of the calculation model;(2) while the current DFLL-TBM design meets the operation shielding requirement of ITER for 4 years, it does not satisfy the tritium self-sufficiency requirement. Compared to the two-step approach, Hydra produces higher accuracies as it does not rely on the homogenization technique during the calculation process. The parallel efficiency tests of Hydra using the DFLL-TBM model also showed that this code maintains a high parallel efficiency on O(100) processors and, as a result, is able to significantly improve computing performance through parallelization. Parameter studies have been carried out by varying the thickness of the beryllium armor layer and the tritium breeding zone to understand the influence of the beryllium layer and breeding zone thickness on tritium breeding performance. This establishes a foundation for further improvement in the tritium production performance of DFLL-TBM.

关键词： Discrete ordinates method DFLL-TBM Neutronic analysis Tritium breeding performance

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