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Asia-Pacific Conference on software Engineering

作者： Shuhao Li Ji Wang Wei Dong Zhi-Chang Qi National Laboratory for Parallel and Distributed Processing Changsha China State Key Laboratory for Software Engineering Wuhan University of China China

Although statecharts has gained widespread use as a formalism for modeling reactive real-time systems, testing these systems still confronts some difficulties, of which a major one is the existence of numerous and complex system behaviors. It is extremely difficult to conduct comprehensive and in-depth testing of such real-time systems. This paper presents an approach to property-oriented real-time testing. Necessary real-time extensions are proposed such that the time-enriched statecharts can describe nontrivial timing constraints. The properties to be tested are characterized by a restricted real-time logic. Then the targeted test sequences are derived from the real-time models according to the user-specified properties. Using this approach, testing efforts can be focused on particular properties of the real-time systems and usually only a small portion of the total behaviors needs to be tested.

关键词： System testing Real time systems software testing software engineering

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Improving the Performance of Lattice Boltzmann Method with Pipelined Algorithm on A Heterogeneous Multi-zone Processor 23rd

Improving the Performance of Lattice Boltzmann Method with...

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23rd International Conference on parallel and distributed Computing, Applications, and Technologies, PDCAT 2022

作者： Zhang, Qingyang Xu, Lei Chen, Rongliang Chen, Lin Chen, Xinhai Wang, Qinglin Liu, Jie Yang, Bo Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410000 China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410000 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9783031299261

Lattice Boltzmann method (LBM) has become a powerful method in computational fluid dynamics and has drawn more and more attention in high-performance computing due to its particulate nature and local dynamics, especially on recent multi-core or many-core platforms. This paper develops a parallel software framework for 3D LBM simulation on a heterogeneous multi-zone processor, MT-3000. An improved pipelined algorithm named Pencil-H is proposed, which can not only fully exploit the advantages of each component of MT-3000 but also overlap the time of calculation and communication. Moreover, an architecture-aware multi-level parallelization algorithm is developed to fully utilize the computational performance of MT-3000. A benchmark test is performed to verify the reliability and test the performance of the LBM code. Experimental results show that the optimized code achieves a 32.02 × speedup compared with using 16 CPU cores and achieves a performance of 286.03MLUPS which reaches 72.3% of the theoretical peak performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational fluid dynamics

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PreCrime to the rescue: Defeating mobile malware one-step ahead 14

PreCrime to the rescue: Defeating mobile malware one-step ah...

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5th ACM Asia-Pacific Workshop on Systems, APSYS 2014

作者： Tan, Cheng Li, Haibo Xia, Yubin Zang, Binyu Chu, Cheng-Kang Li, Tieyan Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Software School Fudan University China State Key Laboratory of Computer Architecture ICT Chinese Academy of Sciences China Huawei Technologies Pte Ltd. Singapore Singapore

ISBN: (纸本)9781450330244

Prior mobile malware defensive means is usually retroactive, which may either lead to high false negatives or can hardly recover systems states from malware activities. PreCrime is a proactive malware detection scheme that detects and stops malware activities from happening. PreCrime creates mirrors of a mobile device in a resource-rich and trusted cloud, which speculatively executes multiple likely user operations concurrently to detect potential tampering and information leakage. Our preliminary evaluation shows that PreCrime introduces small performance overhead on smartphones and feasible delay during speculative execution on the cloud. © 2014 ACM.

关键词： Malware

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3DMeshNet: A Three-Dimensional Differential Neural Network for Structured Mesh Generation

arXiv

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arXiv 2024年

作者： Peng, Jiaming Chen, Xinhai Liu, Jie Laboratory of Digitizing Software for Frontier Equipment Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

Mesh generation is a crucial step in numerical simulations, significantly impacting simulation accuracy and efficiency. However, generating meshes remains time-consuming and requires expensive computational resources. In this paper, we propose a novel method, 3DMeshNet, for three-dimensional structured mesh generation. The method embeds the meshing-related differential equations into the loss function of neural networks, formulating the meshing task as an unsupervised optimization problem. It takes geometric points as input to learn the potential mapping between parametric and computational domains. After suitable offline training, 3DMeshNet can efficiently output a three-dimensional structured mesh with a user-defined number of quadrilateral/hexahedral cells through the feed-forward neural prediction. To enhance training stability and accelerate convergence, we integrate loss function reweighting through weight adjustments and gradient projection alongside applying finite difference methods to streamline derivative computations in the loss. Experiments on different cases show that 3DMeshNet is robust and fast. It outperforms neural network-based methods and yields superior meshes compared to traditional mesh partitioning methods. 3DMeshNet significantly reduces training times by up to 85% compared to other neural network-based approaches and lowers meshing overhead by 4 to 8 times relative to traditional meshing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Mesh generation

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Ruan Jian Xue Bao/Journal of software 2011年第1期22卷 132-148页

作者： Li, Hui-Ba Tian, Tian Peng, Yu-Xing Li, Dong-Sheng Lu, Xi-Cheng National Laboratory for Parallel and Distributed Computing Computer School National University of Defense Technology Changsha 410073 China Institute of Software Computer School National University of Defense Technology Changsha 410073 China

The Internet has become a vital information infrastructure for modern society. However, the concurrent nature of network introduces a wide-range of difficulties in traditional programming methodology in developing high-quality network programs that significantly reduce productivity. The influence of concurrency on the complexity of software development is comparable to the "concurrency crisis" of software brought by multi-core processors, but it receives much less attention here than what it deserves. There is no universal approach to cope with this issue, and there are even disagreements between different approaches. In this paper, the basic concurrency models and their implementations are introduced, and then the paper surveys the inherent complexities of these approaches, comparing their advantages and disadvantages. Finally, this paper offers an opinion on the possibilities for future research on this topic. © Copyright 2011, Institute of software, the Chinese Academy of Sciences. All rights reserved.

关键词： Complex networks

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Implicit Neural Alignment Network for Arbitrary-scale Space-Time Video Super-Resolution

Implicit Neural Alignment Network for Arbitrary-scale Space-...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qin Jiang Qinglin Wang Lihua Chi Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Software for Frontier Equipment Hunan GuoKe Computility Technology Co. Ltd

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Space-time video super-resolution (STVSR) is a comprehensive task comprising two subtasks: video super resolution in space dimension and video frame interpolation in time dimension. Conventional decoupled two-stage approaches tend to overlook the intrinsic correlation between the two tasks. Overcoming this challenge requires the development of a unified model capable of simultaneously implementing space-time super-resolution across arbitrary scales. Most existing models are confined to training on fixed space upsampling scales or specific frame-rate videos, resulting in limited generalization capabilities for flexible space-time super-resolution scenarios. In response to this limitation, our approach draws inspiration from continuous implicit neural representation. We propose an enhanced Implicit Neural Alignment Network (INAN) based on the VideoINR framework, encompassing feature refinement, precise motion flow estimation, and multi-scale feature fusion to optimize the final implicit neural decoding. Our extensive experimental evaluations on multiple benchmarks underscore the efficacy of the INAN model, indicate its superior performance compared to prior STVSR methods.

关键词： Training Interpolation Correlation Superresolution Neural networks Estimation Benchmark testing

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parallel computing for large-scale author name disambiguation in medline 21

Parallel computing for large-scale author name disambiguatio...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Tang, Anyao Wu, Chengkun Liu, Jie Wang, Wei Yang, Xi Xing, Yuting Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410073 China State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781728120584

Author name disambiguation (AND) is an important task in the field of scientific data mining. It has become a great challenge with the rapid growth of academic digital libraries. The task of AND for a large number of authors is computationally intensive. In particular, an author's name in MEDLINE is represented by full last name and initials, like 'Zhang S', which leads to a lot of identical strings that actually represent different names. In this paper, we proposed an efficient algorithm for parallel AND computation. The proposed algorithm mainly addresses the load balancing issue across many computing nodes. It involves the following strategies:(1) Author-based load balancing, which splits the computation load for each core by author name labels. (2) Matrix-based strategy, which calculates the pairwise similarity between publications and saves them in a matrix globally shared by all processes. Then group them by width-first search. We combine the above two strategies, the second of which is used to calculate authors with a large number of documents, and the other authors apply the first. We constructed a publications database written by Chinese authors from MEDLINE, the biggest public database for biomedical literature (abstracts). For benchmark testing, we experimented our algorithm with a dataset of 1 million publications on the Tianhe-2A supercomputer. Firstly, we trained an AND classifier that can achieve 98.1% of F1. The serial computation time is estimated to be approximately 246 hours, while the parallel execution time is approximately 66 hours in the case of four cores on a single node (with a speedup of 3.7x). Finally, we reduced the total parallel computing time of 1 million documents to about 2 hours and achieved 65.8% of parallelism efficiency using 200 cores on 90 nodes. © 2019 IEEE.

关键词： Matrix algebra

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An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data

An unsupervised deep learning framework for gene regulatory ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Guo Mao Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex System National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Recent advances in single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for reconstruction gene regulation networks (GRNs). At present, many different models have been proposed to infer GRN from a large number of RNA-seq data, but most deep learning models use a priori gene regulatory network to infer potential GRNs. It is a challenge to reconstruct GRNs from scRNA-seq data due to the noise and sparsity introduced by the dropout effect. Here, we propose GAALink, a novel unsupervised deep learning method. It first constructs the gene similarity matrix and then refines it by threshold value. It then learns feature representations of genes through a graphical attention autoencoder that propagates information across genes with different weights. Finally, we use gene feature expression for matrix completion such that the GRNs are reconstructed. Compared with seven existing GRNs reconstruction methods, GAALink achieves more accurate performance on seven scRNA-seq dataset with four ground truth networks. GAALink can provide a useful tool for inferring GRNs for scRNA-seq expression data.

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Optimizing Batched Small Matrix Multiplication on Multi-core DSP Architecture 22

Optimizing Batched Small Matrix Multiplication on Multi-core...

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22nd IEEE International Symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Zuo, Xiaohan Xiao, Chunhua Wang, Qinglin Shi, Chen Chongqing University College of Computer Science Chongqing China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha410073 China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment Changsha410073 China

ISBN: (纸本)9798331509712

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many High-Performance Computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x. © 2024 IEEE.

关键词： Network security

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High-performance Audio Matching with Features Learned by Convolutional Deep Belief Network

High-performance Audio Matching with Features Learned by Con...

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Weijiang Feng Naiyang Guan Zhigang Luo Institute of Software College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Audio matching automatically retrieves all excerpts that have the same content as the query audio clip from given audio recordings. The extracted feature is critical for audio matching and the Chroma Energy Normalized Statistics(CENS) feature is the state-of-the-arts. However, CENS might behave unsatisfactorily on some audio because it is a handcraft feature. In this paper, we propose to utilize the features learned by Convolutional Deep Belief Network(CDBN) to enhance the performance of audio matching. Benefit from the strong generalization ability of CDBN, our method works better than CENS based methods on most audio datasets. Since the features learned by CDBN are binary-valued, we can develop a more efficient audio matching algorithm by taking the advantage of this property. Experimental results on both TIMIT dataset and a simulated music dataset confirm effectiveness of the proposed CDBN based method comparing with the traditional CENS feature based algorithm.

关键词： audio matching convolutional deep belief network content-based audio retrieval

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