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14th IEEE International Conference on software Engineering and Service Science, ICSESS 2023

作者： Ma, Penghui Shi, Peichang Yi, Guodong College of Computer Science National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China College of Computer Science National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha410073 China Xiangjiang Lab Changsha410073 China School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha410073 China

ISBN: (纸本)9798350336269

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. © 2023 IEEE.

关键词： Benchmarking

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WPress: An Application-Driven Performance Benchmark for Cloud-Based Virtual Machines

WPress: An Application-Driven Performance Benchmark for Clou...

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International Conference on Enterprise distributed Object Computing (EDOC)

作者： Amir Hossein Borhani Philipp Leitner Bu-Sung Lee Xiaorong Li Terence Hung Institute of High-Performance Computing (IHPC) Singapore s.e.a.l. software evolution & architecture lab University of Zurich Switzerland Parallel and Distributed Computing Center (PDCC) Nanyang Technological University Singapore

ISBN: (纸本)9781479954711

Approaching a comprehensive performance benchmark for on-line transaction processing (OLTP) applications in a cloud environment is a challenging task. Fundamental features of clouds, such as the pay-as-you-go pricing model and unknown underlying configuration of the system, are contrary to the basic assumptions of available benchmarks such as TPC-W or RUBiS. In this paper, we introduce a systematic performance benchmark approach for OLTP applications on public clouds that use virtual machines(VMs). We propose WPress benchmark, which is based on the widespread blogging software, WordPress, as a representative OLTP application and implement an open source workload generator. Furthermore, we utilize a CPU micro-benchmark to investigate CPU performance of cloud-based VMs in greater detail. Average response time and total VM cost are the performance metrics measured by WPress. We evaluate small and large instance types of three real-life cloud providers, Amazon EC2, Microsoft Azure and Rackspace cloud. Results imply that Rackspace cloud has better average response times and total VM cost on small instances. However, Microsoft Azure is preferable for large instance type.

关键词： Benchmark testing Cloud computing Time factors Servers Databases Blogs Publishing

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BGAE: Auto-Encoding Multi-View Bipartite Graph Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 3682-3696页

作者： Li, Liang Pan, Yuangang Liu, Jie Liu, Yue Liu, Xinwang Li, Kenli Tsang, Ivor W. Li, Keqin Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China ASTAR Ctr Frontier AI Res Singapore 138632 Singapore Natl Univ Def Technol Sci & Technol & Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410073 Peoples R China Hunan Univ Supercomp & Cloud Comp Inst Changsha 410073 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Unsupervised multi-view bipartite graph clustering (MVBGC) is a fast-growing research, due to promising scalability in large-scale tasks. Although many variants are proposed by various strategies, a common design is to construct the bipartite graph directly from the input data, i.e., only consider the unidirectional "encoding" process. However, "encoding-decoding" mechanism is a popular design for deep learning, the most representative one is auto-encoder (AE). Enlightened by this, this paper rethinks existing MVBGC paradigms and transfers the "encoding-decoding" design into graph machine learning, and proposes a novel framework termed auto-encoding multi-view bipartite graph clustering (BGAE), which integrates encoding, bipartite graph construction, and decoding modules in a self-supervised learning manner. The encoding module extracts a latent joint representation from the input data, the bipartite graph construction module learns a bipartite graph with connectivity constraint in latent semantic space, and the decoding module recreates the input data via the bipartite graph. Therefore, our novel BGAE combines representation learning, bipartite graph learning, reconstruction learning, and label inference into a unified framework. All the modules are seamlessly integrated and mutually reinforcing for clustering-friendly purposes. Extensive experiments verify the superiority of our novel design and the significance of "decoding" process. To the best of our knowledge, this is the first attempt to explore "encoding-decoding" design in traditional MVBGC.

关键词： Bipartite graph Decoding Machine learning Encoding Synthetic data Matrix decomposition Image reconstruction Bipartite graph learning encoding-decoding graph machine learning multi-view clustering

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MVE-Net: An Automatic 3-D Structured Mesh Validity Evaluation Framework Using Deep Neural Networks

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COMPUTER-AIDED DESIGN 2021年 141卷 103104-103104页

作者： Chen, Xinhai Liu, Jie Gong, Chunye Li, Shengguo Pang, Yufei Chen, Bo Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China China Aerodynam Res & Dev Ctr Mianyang 621000 Sichuan Peoples R China

An important objective of quality control in CFD pre-processing is the facility to indicate to the engineer the validity of the generated mesh. Existing quality measures mainly focus on the subjective evaluation of the shape information of mesh elements, such as aspect ratio, skewness, and shape regularity, and often ignore mesh distribution details. In order to ensure a precise evaluation result, these measures usually work with knowledge-based manual re-evaluation, which heavily increases the meshing cost and hampers automation of the meshing process. In this work, we propose an automatic 3-D structured mesh validity evaluation framework, MVE-Net. It takes mesh files as input, employs deep neural networks to study the role of mesh point distribution on numerical accuracy, and finally outputs the overall validity of the mesh for the simulation. For training the network, we introduce the first 3-D mesh benchmark dataset containing 24576 labeled structured meshes with different models and sizes. The experimental results on the dataset demonstrate the potential of deep neural networks in 3-D mesh validity evaluation and the effectiveness of MVE-Net. The well-trained MVE-Net can be a useful and helpful tool in the fully automatic pre-processing procedure. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Computational fluid dynamics (CFD) Mesh validity Benchmark dataset Neural network

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An efficient heterogeneous parallel algorithm of the 3D MOC for multizone heterogeneous systems

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COMPUTER PHYSICS COMMUNICATIONS 2023年第1期292卷

作者： Li, Runhua Liu, Jie Zhang, Guangchun Gong, Chunye Yang, Bo Liang, Yuechao Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Peoples R China

Recent innovations in powerful high-performance computing (HPC) architecture have enabled high-fidelity whole-core neutron transport simulations in a reasonable amount of time. Among high-fidelity numerical methods, the one-step 3D Method of Characteristic (MOC) is becoming increasingly popular in the reactor design community due to its numerical accuracy, geometrical flexibility and high degrees of parallelism. In this work, we propose a sophisticated heterogeneous parallel algorithm of the 3D MOC based on a supercomputer prototype consisting of brand-new multizone heterogeneous processors called the MT-3000, which has a peak double precision performance of 11.6 TFLOPS when operating at 1.2 GHz. In the 3D MOC calculation procedure, the total run time is dominated by the so-called MOC transport sweep where large amounts of tracks traverse the mesh grids along different directions. We significantly improve the performance of the MOC transport sweep through a three-level parallel algorithm with multiple hiding latency techniques. We have also implemented heterogeneous level-1 basic linear algebra subprograms (BLAS) that's the second computational hot spot. The 3D C5G7 benchmark problem is employed to verify the correctness and efficiency of the heterogeneous parallel 3D MOC algorithm. Numerical results show that it achieves very high performance on the MT-3000-based supercomputer prototype. Specifically, the three-layer parallel algorithm has proven to be highly scalable for each layer, and the Quarter-MT-3000 performance of the MOC transport sweep has achieved 23.6% of the peak performance, which is close to the theoretical upper limit.& COPY;2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).

关键词： Method of characteristics Neutron transport Reactor simulation Heterogeneous parallel High performance computing MT-3000

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Atomic-action-based Contrastive Network for Weakly Supervised Temporal Language Grounding

Atomic-action-based Contrastive Network for Weakly Supervise...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wu, Hongzhou Lyu, Yifan Shen, Xingyu Zhao, Xuechen Wang, Mengzhu Zhang, Xiang Luo, Zhigang Natl Univ Def Technol Parallel & Distributed Proc Lab Changsha Peoples R China Univ Chinese Acad Sci Inst Software Chinese Acad Sci Beijing Peoples R China Natl Univ Def Technol Sch Comp Changsha Peoples R China Natl Univ Def Technol Inst Quantum Informat Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha Peoples R China

ISBN: (纸本)9781665468916

As one knows, an event often consists of several actions while each action is atomic. Inspired by this insight, we propose a novel framework named Atomic-action-based Contrastive Network model (ACN) for weakly supervised temporal language grounding task to localize the query-related event moment in an untrimmed video, without access to any temporal annotations. Specifically, ACN first determines the accurate moment boundary of each action in a query-agnostic way. This can adequately exploit homogeneous visual cues while impeding the heterogeneity of the query from hurting the atomicity of visual action, i.e., action boundary. To effectively localize the query-related event, we seek the discriminative words in the given query, and explore a composite-grained contrastive module to retrieve those corresponding atomic actions in the common latent space across modalities. This boosts feature discrimination of visual event segment to remove irrelevant action video segments. Experiments on two popular datasets show the efficacy of our model.

关键词： weakly supervised temporal language grounding cross-modal interaction contrastive learning atomic action discriminative word

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FENSE: A feature-based ensemble modeling approach to cross-project just-in-time defect prediction

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EMPIRICAL software ENGINEERING 2022年第7期27卷 162-162页

作者： Zhang, Tanghaoran Yu, Yue Mao, Xinjun Lu, Yao Li, Zhixing Wang, Huaimin Natl Univ Def Technol Key Lab Software Engn Complex Syst Changsha Hunan Peoples R China Natl Univ Def Technol Key Lab Parallel & Distributed Comp Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

Context: Just-in-time defect prediction (JITDP) leverages modern machine learning models to predict the defect-proneness of commits. Such models require adequate training data, which is unavailable in projects with short histories. To address this problem, cross-project methods reuse the data or models in other projects to make predictions, grounded on the assumption that they share similar defect-related features. However, these features are overlooked, which leads to unsatisfying model performance. Objective: This study aims to investigate the relationship between cross-project JITDP performances and project features, thereby improving the performance of cross-project models. Method: We propose a F eature-based ENSE mble modeling approach (FENSE) to cross-project JITDP. For a target project, FENSE pairs it to each source project and obtains 20 features. Leveraging them, it can predict the transferability of each off-the-shelf JITDP model. Then FENSE identifies the most transferable ones and combines them to make cross-project predictions. To achieve this, we conduct a large-scale empirical study of 113,906 project pairs in GitHub and investigate the impact of project features. Results: The results show that: (1) cross-project transferability is highly related to features including programming language and the defect ratio of the source project;(2) our feature-based model selection scheme can improve the cross-project JITDP performance by 10%;(3) FENSE outperforms other models on five evaluation measures without extra time and space costs. Conclusions: Our study suggests that project features can help identify powerful cross-project JITDP models and improve the performance of ensemble approaches.

关键词： Just-in-time defect prediction Cross project Ensemble learning Model selection software project feature

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A linearized coarse mesh finite difference preconditioner for the within-group Krylov subspace iteration based on two-dimensional method of characteristics

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ANNALS OF NUCLEAR ENERGY 2021年 151卷 107889-107889页

作者： Zhang, Guangchun Li, Zhifeng Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China South China Univ Technol Sch Elect Power Guangzhou 510640 Peoples R China

In this work, a preconditioner is developed based on the linear coarse mesh finite difference (CMFD) formulation for the flexible generalized minimal residual (FGMRES) algorithm, and applied to accelerate within-group Krylov iterations based on the two-dimensional (2-D) method of characteristics (MOC). The conventional CMFD method is linearized by replacing the multiplicative updating operator with an additive correction operator in the prolongation step. The effectiveness of the linearized CMFD preconditioner for problems featuring steep flux gradients and high scattering ratios can be demonstrated by the numerical results for the IAEA LWR pool reactor problem. The total FGMRES iterations and computing time were decreased by 52.7% and 41.8%, respectively. However, only modest efficiency improvements were achieved for the 2-D C5G7 and the KAIST-2A benchmark problems, revealing the degraded performance of the linearized CMFD preconditioner for problems with strong local heterogeneities. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Method of characteristics Krylov subspace iteration method Preconditioner IAEA LWR pool reactor problem 2-D C5G7 benchmark problem KAIST-2A benchmark problem

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Towards more effective and economic sparsely-activated model

arXiv

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arXiv 2021年

作者： Jiang, Hao Zhan, Ke Qu, Jianwei Wu, Yongkang Fei, Zhaoye Zhang, Xinyu Chen, Lei Dou, Zhicheng Qiu, Xipeng Guo, Zikai Lai, Ruofei Wu, Jiawen Hu, Enrui Zhang, Yinxia Jia, Yantao Yu, Fan Cao, Zhao Distributed and Parallel Software Lab Huawei China School of Computer Science Fudan University Department of Computer Science and Engineering Hong Kong University of Science and Technology Gaoling School of Artificial Intelligence Renmin University of China

The sparsely-activated models have achieved great success in natural language processing through large-scale parameters and relatively low computational cost, and gradually become a feasible technique for training and implementing extremely large models. Due to the limit of communication cost, activating multiple experts is hardly affordable during training and inference. Therefore, previous work usually activate just one expert at a time to alleviate additional communication cost. Such routing mechanism limits the upper bound of model performance. In this paper, we first investigate a phenomenon that increasing the number of activated experts can boost the model performance with higher sparse ratio. To increase the number of activated experts without an increase in computational cost, we propose SAM (Switch and Mixture) routing, an efficient hierarchical routing mechanism that activates multiple experts in a same device (GPU). Our methods shed light on the training of extremely large sparse models and experiments prove that our models can achieve significant performance gain with great efficiency improvement. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

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LRCB: A Comprehensive Benchmark Evaluation of Reference-free Lossless Compression Tools for Genomics Sequencing Long Reads Data

LRCB: A Comprehensive Benchmark Evaluation of Reference-free...

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Data Compression Conference (DCC)

作者： Hui Sun Huidong Ma Yingfeng Zheng Haonan Xie Meng Yan Cheng Zhong Xiaoguang Liu Gang Wang Nankai-Baidu Joint Lab Parallel and Distributed Software Technology Lab College of Computer Science Nankai University Tianjin China School of Electrical Engineering Guangxi University Nanning China School of Computer Electronics and Information Guangxi University Nanning China

ISBN: (数字)9798350385878

ISBN: (纸本)9798350385885

The advancement of long reads sequencing technologies has led to a significant increase in biological sequencing big data. Although several reference-free compressors are available for saving long reads data storage space, choosing the suitable one is challenging due to the shortage of thorough and systematic evaluations of their lossless compression effectiveness, both dedicated and general-purpose. In this study, we performed benchmark examinations on 30 compressors, including 11 specialized for long reads and 19 general-purpose ones, using 31 real-world datasets with differing sequencing platforms, species, and lengths. Each lossless compressor was evaluated on 13 performance measures, including compression strength, compression robustness, as well as time and peak memory required for compression and decompression. Additionally, for future long reads data compressors, we outlined investigation directions with consideration for privacy-sensitive sequences data security, hardware parallel acceleration, parameter tuning framework, and system hardware-algorithm integration design. We summarized the results as the Long Reads Compression Benchmark, available at https://***/fahaihi/LRCB.

关键词： Sequential analysis Systematics Memory management Benchmark testing Compressors Loss measurement Time measurement

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