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检索条件"任意字段=International Symposium on Software Engineering for Parallel and Distributed Systems"

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Job Rotation for the Competencies Maintaining: A Case Study in IT Project Management 20th

Job Rotation for the Competencies Maintaining: A Case Study ...

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20th international symposium on distributed Computing and Artificial Intelligence, DCAI 2023

作者： Szwarc, Eryk Golińska-Dawson, Paulina Bocewicz, Grzegorz Banaszak, Zbigniew Faculty of Electronics and Computer Science Koszalin University of Technology Sniadeckich 2 Koszalin75-453 Poland Faculty of Engineering Management Poznan University of Technology ul. Jacka Rychlewskiego 2 Poznań60-965 Poland

ISBN: (纸本)9783031383175

This paper deals with scheduling in software development organizations. A new version of the software project scheduling problem (SPSP) is considered that takes into account the phenomenon of learning and forgetting. Their impact on maintaining the competencies level of employees is important, especially in case of occurrence of unplanned events, such as absenteeism and/or change in the priorities of projects. The paper proposes a new reference declarative SPSP model for proactive planning of assignments of multi-skilled programmers to tasks in the portfolio of a network of activities in IT projects, taking into account forgetting effects. It’s implementation in the constraints programming environment, enables decision-making in project management at software companies. It helps to solve the problem of finding a feasible solution, and identifying conditions under which given expectations are met. The potential of the proposed SPSP model is illustrated by numerical examples. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： software design

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MICVIF: an automatic unsupervised feature selection method for software defect prediction

MICVIF: an automatic unsupervised feature selection method f...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Wenzhi Zhu Zhiqiang Li Haiyang Liu School of Compute Science Shaanxi Normal University Xi’an China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The presence of correlated and redundant software metrics can significantly impact the performance of defect prediction models. Existing studies often rely on feature selection techniques to mitigate these issues. In this paper, an automatic unsupervised feature selection approach is proposed for software defect prediction, called MICVIF. It combines the maximum information coefficient (MIC) and variance inflation factor (VIF) techniques to automatically select useful metric subsets. MIC is used to compute the correlations between each pair of metrics to address collinearity, while VIF is employed to compute the correlations between one metric and multiple others to handle multicollinearity. Finally, we compare MICVIF with 8 competing feature selection methods across 4 supervised and unsupervised defect prediction models on 29 releases across 12 open-source software projects, and the final results validate the effectiveness of our approach.

关键词： Microwave integrated circuits Correlation software algorithms Predictive models Feature extraction Reliability engineering Prediction algorithms software software reliability software measurement

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FlowEmbed: Binary function embedding model based on relational control flow graph and byte sequence 29

FlowEmbed: Binary function embedding model based on relation...

引用

29th IEEE international Conference on parallel and distributed systems, ICPADS 2023

作者： Wang, Yongpan Dong, Chaopeng Li, Siyuan Luo, Fucai Su, Renjie Song, Zhanwei Li, Hong Chinese Academy of Sciences Institute of Information Engineering China University of Chinese Academy of Sciences School of Cyber Security China State Grid Fujian Electric Power Company China

ISBN: (纸本)9798350330717

Binary function embedding models are applicable to various downstream tasks within IoT device software systems and have demonstrated advantages in numerous binary analysis tasks, such as vulnerability (homologous) function search and compilation optimization option identification. However, current binary function embedding methods either learn embedding based on code sequence, which lack the program semantics of functions (e.g., control flow, etc.) or based on program structure graphs, which omit global sequential information. As a result, these methods fall short in enabling models to learn the complete semantic of function. In this paper, we introduce FlowEmbed, a novel approach that synergistically integrates control flow and global semantic learning to facilitate exhaustive code comprehension. Initially, FlowEmbed harnesses a distinct relational control flow graph combined with the power of BERT and RGCN models to aptly capture the nuances of control flow semantics. Moreover, by deploying the DPCNN model on a byte sequence constructed from function machine code, FlowEmbed adeptly discerns the inherent global sequential semantics of binary functions. Through rigorous evaluations spanning three IoT-related tasks, FlowEmbed's efficacy becomes evident, showcasing notable improvements: a 20.6% improvement in compilation optimization option identification, a 1.8% improvement in binary function similarity analysis, and an 11.9% improvement in homologous function search. Collectively, these results underscore FlowEmbed's superior capability, positioning it as a invaluable asset in a binary analysis application. © 2023 IEEE.

关键词： binary code search binary code similarity detection binary function embedding deep learning static analysis

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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...

引用

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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FreezePipe: An Efficient Dynamic Pipeline parallel Approach Based on Freezing Mechanism for distributed DNN Training 26

FreezePipe: An Efficient Dynamic Pipeline Parallel Approach ...

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26th international Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Weng, Caishan Shu, Zhiyang Xu, Zhengjia Zhang, Jinghui Luo, Junzhou Dong, Fang Wang, Peng Wang, Zhengang Southeast University School of Computer Science and Engineering Nanjing China Boston College Nanjing China Co. Ltd China Jiangsu HHigh-Tech Software Technology Co.Ltd China

ISBN: (纸本)9798350331684

Deep Neural Network (DNN) training on a large scale is extremely time-consuming and computationally intensive, which is accelerated by distributed training. In recent years, pipeline parallelism has been developed, which enables partitioning the model across several devices, e.g. GPU, and training efficiency is improved by dividing data batches into micro-batches, with each of them processed by a different stage of the model. Currently, parallel training assumes pipeline placement and partitioning are static, with parameters updating each iteration, without accounting for freezing. This results in computational resources not being fully utilized. In this paper, we propose FreezePipe, a novel method for optimizing deep learning training that combines the freezing mechanism with pipeline parallel training. In FreezePipe, a lightweight method for determining the freezing strategy based on gradient changes is employed. Considering that resources need to be released based on the frozen layer, a lightweight model partitioning algorithm was designed to determine the optimal strategy for pipeline partitioning. Experimental results show that FreezePipe can reduce the training time by 64.5% compared to Torchgpipe on CIFAR-10 dataset without compromising any model performance. © 2023 IEEE.

关键词： Pipelines

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Cause-effect Graphing Technique: A Survey of Available Approaches and Algorithms 24

Cause-effect Graphing Technique: A Survey of Available Appro...

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24th IEEE/ACIS international Winter Conference on software engineering, Artificial Intelligence, Networking and parallel/distributed Computing, SNPD 2022

作者： Krupalija, Ehlimana Cogo, Emir Becirovic, Seila Prazina, Irfan Besic, Ingmar University of Sarajevo Faculty of Electrical Engineering Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350310412

Cause-effect graphs are often used as a method for deriving test case suites for black-box testing different types of systems. This paper represents a survey focusing entirely on the cause-effect graphing technique. A comparison of different available algorithms for converting cause-effect graph specifications to test case suites and problems which may arise when using different approaches are explained. Different types of graphical notation for describing nodes, logical relations and constraints used when creating cause-effect graph specifications are also discussed. An overview of available tools for creating cause-effect graph specifications and deriving test case suites is given. The systematic approach in this paper is meant to offer aid to domain experts and end users in choosing the most appropriate algorithm and, optionally, available software tools, for deriving test case suites in accordance to specific system priorities. A presentation of proposed graphical notation types should help in gaining a better level of understanding of the notation used for specifying cause-effect graphs. In this way, the most common mistakes in the usage of graphical notation while creating cause-effect graph specifications can be avoided. © 2022 IEEE.

关键词： Black-box testing

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Dancing in the Dark: Profiling for Tiered Memory 35

Dancing in the Dark: Profiling for Tiered Memory

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Choi, Jinyoung Blagodurov, Sergey Tseng, Hung-Wei Univ Calif Riverside Riverside CA 92521 USA Adv Micro Devices Inc Sunnyvale CA 94088 USA

ISBN: (纸本)9781665440660

With the DDR standard facing density challenges and the emergence of the non-volatile memory technologies such as Cross-Point, phase change, and fast FLASH media, compute and memory vendors are contending with a paradigm shift in the datacenter space. The decades-long status quo of designing servers with DRAM technology as an exclusive memory solution is likely coming to an end. Future systems will increasingly employ tiered memory architectures (TMAs) in which multiple memor) , technologies work together to satisfy applications' ever-growing demands for more memory, less latency, and greater bandwidth. Exactly how to expose each memory type to software is an open question. Recent systems have focused on hardware caching to leverage faster DRAM memory while exposing slower non-volatile memory to OS-addressable space. The hardware approach that deals with the non-uniformity of TMA, however, requires complex changes to the processor and cannot use fast memory to increase the system's overall memory capacity. Mapping an entire TMA as OS-visible memory alleviates the challenges of the hardware approach but pushes the burden of managing data placement in the TMA to the software layers. The software, however, does not see the memory accesses by default;in order to make informed memory-scheduling decisions, software must rely on hardware methods to gain visibility into the load/store address stream. The OS then uses this information to place data in the most suitable memory location. In this paper, we evaluate different methods of memory-access collection and propose a hybrid tiered-memory approach that offers comprehensive visibility into TMA.

关键词： Nonvolatile memory Memory management Memory architecture Random access memory Trademarks Media software

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Equivalence of Denotational and Operational Semantics for Interaction Languages 1

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16th international symposium on Theoretical Aspects of software engineering (TASE)

作者： Mahe, Erwan Gaston, Christophe Le Gall, Pascale Univ Paris Saclay List CEA F-91120 Palaiseau France Univ Paris Saclay Cent Supelec F-91192 Gif Sur Yvette France

ISBN: (数字)9783031103636

ISBN: (纸本)9783031103636;9783031103629

Message Sequence Charts (MSC) and Sequence Diagrams (SD) are graphical models representing the behaviours of distributed and concurrent systems via the scheduling of discrete emission and reception events. So as to exploit them in formal methods, a mathematical semantics is required. In the literature, different kinds of semantics are proposed: denotational semantics, well suited to reason about algebraic properties and operational semantics, well suited to establish verification algorithms. We define an algebraic language to specify so-called interactions, similar to the MSC and SD models. It is equipped with a denotational semantics associating sets of traces (sequences of observed events) to interactions. We then define a structural operational semantics in the style of process algebras and prove the equivalence of the two semantics.

关键词： Interactions Sequence diagrams distributed & concurrent systems Formal language Denotational semantics Operational semantics

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parallel Swarm Propagation for Neural Networks

Parallel Swarm Propagation for Neural Networks

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parallel Computing and distributed systems (PCDS), international symposium on

作者： Arghya Bhattacharya V Premanand School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Deep learning has revolutionized various fields but faces challenges such as limitations owing to vanishing gradients and the inability to handle non-differentiable loss functions and layers. Our study proposes parallel Swarm Propagation for Neural Networks, a novel approach that leverages parallel particle swarm optimization (PSO) for optimizing neural networks. PSO does not require gradient calculations and effectively circumvents the vanishing gradient problem. This enables the exploration of non-differentiable loss functions and layers, potentially leading to more robust and flexible deep-learning models. Additionally, the inherent parallelism of PSO allows for significant performance gains by utilizing the ever-increasing processing power of parallel-computing architectures. Our study investigates the efficacy of parallel PSO in optimizing neural networks, analyzing its performance compared to traditional gradient-based methods, and exploring its potential for unlocking new possibilities in deep learning.

关键词： Backpropagation Training Deep learning Scalability Neural networks Graphics processing units parallel processing Network architecture Hardware Particle swarm optimization

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YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment 41st

YFLM: An Improved Levenberg-Marquardt Algorithm for Global ...

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41st Computer Graphics international Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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