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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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Smart Contract Vulnerability Detection via Feature Fusion of Local Data Flow and Global Features 22

Smart Contract Vulnerability Detection via Feature Fusion of...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Wang, Long Chen, Zhihua Pang, Hua Li, Xiaoguang Liaoning University School of Cyber Science and Engineering Shenyang China Shenyang Normal University School of Mathematics and Systems Science Shenyang China

ISBN: (纸本)9798331509712

Smart contracts typically involve the transaction and management of digital assets in blockchain, and their security has always been a focal point. Current vulnerability detection methods often suffer from low efficiency, high costs, and limited accuracy. To address these challenges, this paper proposes a vulnerability detection method that fuses local data flow and global structural features. This method comprises two sub-networks: DFGPNet and SASTNet. The DFGPNet sub-network takes the contract source code and its corresponding data flow graph (DFG) as input, using a pre-trained model based on programming languages to extract local vulnerability features, focusing on the key data flow information of contract vulnerabilities. The SASTNet subnetwork serializes the abstract syntax tree (AST) corresponding to the contract as input, and extracts global vulnerability features by combining a bidirectional long short-term memory network (BiLSTM) with a self-attention mechanism, focusing specifically on the global structural information of contract vulnerabilities. The features from both sub-networks are fused and passed through a fully connected layer and sigmoid function for vulnerability prediction. To validate the model's effectiveness, experiments on a dataset of 26,080 smart contracts, covering three common vulnerability types, demonstrate that the proposed method outperforms seven state-of-the-art detection techniques in precision, recall, and F1 score. © 2024 IEEE.

关键词： Trees (mathematics)

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Optimizing the design of parallel busbars to suppress the Taylor Instability in large-scale liquid metal batteries 3

Optimizing the design of parallel busbars to suppress the Ta...

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2024 3rd international symposium on New Energy Technology Innovation and Low Carbon Development, NET-LC 2024

作者： Zuo, Xiaozhong Chen, Baozhi Li, Qiguang Li, Benwen Li, Chenhao Liu, Ke Guo, Jingru School of Energy and Power Engineering Dalian University of Technology Liaoning Dalian116024 China

In this study, we utilize the open-source software OpenFOAM to numerically simulate the Tayler instability in liquid metal batteries. The simulation depicts the dynamic evolution of the electrolyte layer, fluid flow, and magnetic field distribution in the battery at different operating times. Special attention is paid to the influence of optimizing the parallel busbar configuration on Tayler instability and battery capacity. It is found that the additional magnetic field generated by the busbars can effectively alter the magnetic field distribution inside the battery, thereby significantly reducing the Lorentz force and suppressing the Tayler instability within the battery. Therefore, by optimizing the parallel busbar configuration, the stable operating time of the battery is prolonged, and the battery capacity is enhanced. This study provides theoretical support and practical guidance for the application of liquid metal batteries in large-scale grid-level energy storage systems and offers new insights into the development of energy storage technology. © Published under licence by IOP Publishing Ltd.

关键词： Lorentz force

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Enhancing Security and Performance in PyTorch: A Hybrid Fuzzing Approach

Enhancing Security and Performance in PyTorch: A Hybrid Fuzz...

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

作者： Varun Chawla Independent Researcher

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

As software systems become more intricate, the prevalence of memory safety vulnerabilities poses a growing concern for security. This study delves into the realm of soft-ware security, focusing on PyTorch, a popular machine-learning framework. Leveraging hybrid fuzzing techniques, the research aims to identify and address memory safety issues in PyTorch, emphasizing its significance in the context of AI applications. The study introduces improvements to the sydr-fuzz tool, optimizing its security predicates verification and implementing a novel scheduling strategy for memory modeling. The findings include the discovery of multiple security vulnerabilities in PyTorch and demonstrate notable enhancements in the performance of the hybrid fuzzing tool.

关键词： Analytical models Processor scheduling Computational modeling Refining Fuzzing software systems Data models Safety Security Optimization

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Bandwidth Characterization of DeepSpeed on distributed Large Language Model Training

Bandwidth Characterization of DeepSpeed on Distributed Large...

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2024 IEEE international symposium on Performance Analysis of systems and software, ISPASS 2024

作者： Hanindhito, Bagus Patel, Bhavesh John, Lizy K. The University of Texas at Austin AustinTX United States Dell Technologies Round RockTX United States

ISBN: (纸本)9798350376388

The exponential growth of the training dataset and the size of the large language model (LLM) significantly outpaces the incremental memory capacity increase in the graphics pro-cessing units (GPUs). Thousands of GPUs are needed to handle state-of-the-art models, which require building an expensive AI GPU cluster that is out of reach for most researchers. This not only makes the cost to train the model more costly but also signifies the environmental impact. To improve the efficiency and scalability of existing infrastructure to handle increasingly demanding training tasks, Microsoft released DeepSpeed, an open-source optimization library for PyTorch that can easily be integrated into existing training flow with minimal code changes. This paper presents a comprehensive third-party evaluation of DeepSpeed for training GPT-2-like LLM on mainstream GPU clusters that are more accessible to everyone. The evaluation includes memory usage analysis and bandwidth characterization in addition to the achieved model size and the attained compute throughput to help compare horizontal and vertical scaling. First, we examine the DeepSpeed ZeRO in single- and dual-node training against the popular distributed training libraries: PyTorch distributed Data-parallel (DDP) with data parallelism and Megatron-LM with data and model parallelism. While DDP achieves higher throughput due to less communication, the model size is limited to a single GPU memory capacity. In single-node training, Megatron-LM can fit a 4x larger model than the DDP, while ZeRO can handle a model with 0.8x-l.2x size of the Megatron-LM. Both Megatron-LM and ZeRO are reasonably competitive in terms of throughput. However, in dual-node training, Megatron- Lmsees a significant drop in throughput due to the excessive inter-node communication, achieving only 25 %-30 % of the throughput offered by ZeRO. Secondly, we evaluate ZeRO-Offload to consolidate multi-node training into single-node. With CPU offloading, ZeRO-Offloa

关键词： Bandwidth

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Multi-Step Edge Cloud Load Prediction by Analyzing Behavior of Workload Groups 29

Multi-Step Edge Cloud Load Prediction by Analyzing Behavior ...

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

作者： Wu, Xiaojun Huang, Wenxing Yuan, Sheng Mi, Jiahao Xi'an Jiaotong University School of Software Engineering Shaanxi Joint Laboratory of Artificial Intelligence Xi'an China

ISBN: (纸本)9798350330717

With the flourishing development of the Internet of Things (IoT) era, Edge Computing (EC) technology has garnered significant attention along with advancements in communication and IoT technologies. Effectively harnessing the computational resources in the edge environment has become a paramount concern. Accurate workload prediction is considered fundamental to optimizing the utilization of limited edge resources. However, most existing edge cloud load prediction approaches overlook the correlations among edge sites. Moreover, for cloud-native applications, user requests are typically handled by multiple containers. By evolving the work behavior of workload groups, it is possible to achieve a shift from focusing on individual containers to the collective, resulting in higher predictive accuracy. In this paper, the behavior of workload groups is analyzed through the reference to both static and dynamic container information. Leveraging the input data constructed based on the behavior of workload groups, an improved Sample Convolution and Interaction Network (SCINet) is employed for early multi-step prediction of edge container loads. Experimental validation on an edge cloud load dataset demonstrates the effectiveness of the proposed approach. Experimental results show that the proposed method can effectively improve the accuracy of edge cloud load prediction. © 2023 IEEE.

关键词： Edge Computing SCINet Workload Prediction

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24th international Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2023

24th International Conference on Parallel and Distributed Co...

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24th international Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2023

ISBN: (纸本)9789819982103

The proceedings contain 28 papers. The special focus in this conference is on parallel and distributed Computing, Applications and Technologies. The topics include: Insider Trading Detection Algorithm in Industrial Chain Based on Logistics Time Interval Characteristics;link Attributes Based Multi-service Routing for software-Defined Satellite Networks;A Fuzzy Logical RAT Selection Scheme in SDN-Enabled 5G HetNets;SSR-MGTI: Self-attention Sequential Recommendation Algorithm Based on Movie Genre Time Interval;fine Time Granularity Allocation Optimization of Multiple Networks Industrial Chains in Task Processing systems;Ε-Maximum Critic Deep Deterministic Policy Gradient for Multi-agent Reinforcement Learning;effective Density-Based Concept Drift Detection for Evolving Data Streams;an End-to-End Multiple Hyper-parameters Prediction Method for distributed Constraint Optimization Problem;Formalization and Verification of the Zab Protocol Using CSP;dynamic Priority Coflow Scheduling in Optical Circuit Switched Networks;Deep Reinforcement Learning Based Multi-WiFi Offloading of UAV Traffic;Triple-Path RNN Network: A Time-and-Frequency Joint Domain Speech Separation Model;design of Query Based Gallery Selector and Mask-Aware Loss for Person Search;a Privacy-Preserving Blockchain Scheme for the Reliable Exchange of IoT Data;R-RPT-A Reliable Routing Protocol for Industrial Wireless Sensor Networks;action Segmentation Based on Encoder-Decoder and Global Timing Information;Security Challenges and Lightweight Cryptography in IoT: Comparative Study and Testing Method for PRESENT-32bit Cipher;The Prediction Model of Water Level in Front of the Check Gate of the LSTM Neural Network Based on AIW-CLPSO;Using MPIs Non-Blocking Allreduce for Health Checks in Dynamic Simulations;parallelizable Loop Detection using Pre-trained Transformer Models for Code Understanding;list-Based Workflow Scheduling Utilizing Deep Reinforcement Learning.

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ODRL: Reinforcement Learning in Priority Scheduling for Running Cost Optimization 29

ODRL: Reinforcement Learning in Priority Scheduling for Runn...

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

作者： Kuang, Cheng Duan, Meng Lv, Tao Wu, Yingjun Li, Li Wang, Luping Software Technology Co. Ltd Cloud Native Platform Team Suzhou China Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou China

ISBN: (纸本)9798350330717

Due to the heterogeneity of workloads and the randomness and complexity of hybrid scheduling environment, How to address the challenge of minimizing data center running cost while ensuring workloads SLA has emerged as a significant research problem. To address this, we propose an Optimal Deep Reinforcement Learning Model for Running Cost Optimization in Hybrid Environments (ODRL). Firstly, we propose Running Cost Model (RCM) that analyzes data center running costs from two perspectives based on different running patterns of the workload and node affinity constraints, minimizing computing node utilization and resource running costs. Subsequently, we propose a Priority-Aware Scheduling Algorithm based on Deep Reinforcement Learning(PASD) that use AHP-TOPSIS to quantify workload characterization, get real-time feedback for environment and update historical experience through prioritized experience replay. Finally, extensive experimental results validate the effectiveness of the proposed model. In comparison to Kube-Scheduler, ODRL achieves a significant 15% improvement in reducing data center running cost. Additionally, there is a 7.8% increase in CPU utilization and a 16.7% increase in memory utilization. © 2023 IEEE.

关键词： Cloud computing Priority scheduling Reinforcement learning Running cost

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Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer 36

Proteome-scale Deployment of Protein Structure Prediction Wo...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Gao, Mu Coletti, Mark Davidson, Russell B. Prout, Ryan Abraham, Subil Hernandez, Benjamin Sedova, Ada Georgia Inst Technol Ctr Study Syst Biol Atlanta GA 30332 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN USA Oak Ridge Natl Lab Biosci Div Oak Ridge TN 37830 USA Oak Ridge Natl Lab Natl Ctr Computat Sci Oak Ridge TN USA

ISBN: (纸本)9781665497473

Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now approaching the accuracy of crystallographic experiments, and with accelerators like GPUs and TPUs making inference using large models rapid, genome-level structure prediction becomes an obvious aim. Leadership-class computing resources can be used to perform genome-scale protein structure prediction using state-of-the-art deep learning models, providing a wealth of new data for systems biology applications. Here we describe our efforts to efficiently deploy the AlphaFold v.2 program, for full-proteome structure prediction, at scale on the Oak Ridge Leadership Computing Facility's resources, including the Summit supercomputer. We performed inference to produce the predicted structures for 40,526 protein sequences, corresponding to four prokaryotic proteomes and one plant proteome, using under 4,400 total Summit node hours, equivalent to using the majority of the supercomputer for a little over one hour. We also designed an optimized structure refinement that reduced the time for the relaxation stage of the AlphaFold pipeline by over 10X for longer sequences. We demonstrate the types of analyses that can be performed on proteome-scale collections of sequences, including a search for novel quaternary structures and implications for functional annotation.

关键词： high-performance computing protein structure prediction deep learning proteomics workflow management software

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Online public opinion time series prediction based on improved N-Beat and multimodal hybrid fusion 22

Online public opinion time series prediction based on improv...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Tian, Yuling Miao, Shengfa Yao, Shaowen Wang, Puming Jin, Xin Ma, Shichao Engineering Research Center of Cyberspace Yunnan University School of Software Yunnan University Kunming China

ISBN: (纸本)9798331509712

Social media offers a promising way to analyze online public opinion, which has drawn extensive attention from various sectors. In academia, most studies focus on predicting public opinion using unimodal time series methods, paying little attention to multimodal approaches. However, public opinion may be affected by various complex social factors, so it is necessary to explore multimodal elements. Based on the N-Beats model, we propose a novel model, HFN-BeatsConv, which employs a powerful modal alignment strategy, 3D-TCN. Most fuses multimodal data for public opinion prediction. The model employs a component, 3D-TCN, for modal alignment, differs from other research in that it focuses on the time at which the text appears. Subsequently, the model, HFN-BeatsConv, the N-Beats model is enhanced through the utilization of 3D-TCN, which enables the processing of multivariate time series data and the reduction of multimodal time series forecast error. To increase the usability and sustainability of research, this study provides a valuable social media dataset, as a supplementary feature of time series prediction. Through extensive experiments, the proposed method outperforms the existing methods. © 2024 IEEE.

关键词： Prediction models

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ATL: A Link Failure Recovery Method with Fast Recovery Speed, Low Interruption Rate, and Small TCAM Consumption in SDN 29

ATL: A Link Failure Recovery Method with Fast Recovery Speed...

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

作者： Zhang, Xinyu Chen, Jue Shanghai University of Engineering Science School of Electronic and Electric Engineering Shanghai China

ISBN: (纸本)9798350330717

Based on the architecture of the software Defined Network (SDN), we argue that SDN together with Internet of Things (IoT) applications can enhance the control and management of IoT in terms of flexibility and intelligence. However, with the rapid development of SDN, link fault tolerance has become a crucial challenge in ensuring network reliability and stability. In this field, the current solutions mainly consist of proactive and reactive schemes, but there are still limitations and challenges. The proactive solutions often waste significant TCAM resources, while the reactive solutions face longer fault recovery time. In order to solve the shortcoming of both solutions, and to address the issues of traffic interruption caused by insufficient link bandwidth after link failures, this paper proposes a method called Adapt To Link (ATL) which combines the greedy algorithm with Pro-VLAN technology (a proactive method which calculates a backup path and assigns a VLAN ID for each link before link failures), aiming to achieve efficient fault-tolerant recovery for link failures in the SDN data plane. In this algorithm, we utilize the Pro-VLAN method and pre-compute multiple backup paths for each link before a failure occurs. When a link failure occurs, we can swiftly switch the affected traffic to the backup paths automatically without the controller, ensuring continuous flow of network traffic. Additionally, we introduce a greedy algorithm to satisfy the requirement of elephant flows, thereby improving network resource utilization and performance. By combining these techniques and methods, our algorithm exhibits the characteristics of low interruption rate, minimal resource consumption, and high efficiency. The experimental results demonstrate that under high load conditions, the ATL scheme reduces the interruption rate by 72.4% compared to the proactive approach and by 54.9% compared to the reactive approach, respectively. The number of additional flow rules in ATL is only

关键词： Fault Tolerance Heuristic Algorithm Proactive Approach Reactive Approach software Defined Network

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