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检索条件"任意字段=2nd International Symposium on Parallel and Distributed Processing and Applications"

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MonkeyGPT: Generative AI in Network Anomaly Detection of Video Conference applications 22

MonkeyGPT: Generative AI in Network Anomaly Detection of Vid...

引用

22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Xu, Junhao He, Dongbiao Zhang, Chen Zhou, Xu Ming, Zhongxing Cui, Laizhong Sangfor Technologies Inc China Shenzhen University College of Computer Science and Software Engineering China China

ISBN: (纸本)9798331509712

The rapid advancement of generative artificial intelligence (GAI) has led to the creation of transformative applications such as ChatGPT, which significantly boosts text processing efficiency and diversifies audio, image, and video content. Beyond digital content creation, GAI's capability to analyze complex data distributions holds immense potential for next-generation networks and communications, especially given the swift rise of video conferencing applications (VCAs). This paper presents a dynamic, real-time method for detecting anomalous network links in video conferencing applications. The proposed tool, MonkeyGPT, generates tracing representations of network activity and trains a large language model from scratch to serve as a detection system based on network traffic data. Unlike traditional methods, MonkeyGPT provides an unrestricted search space and does not rely on predefined rules or patterns, enabling it to detect a wider range of anomalies. We demonstrate the effectiveness of MonkeyGPT as an anomaly detection tool in real-world VCAs. The results indicate that the model possesses strong detection capabilities, achieving an accuracy rate of over 97%. It is applicable to various platforms, including Zoom, Microsoft Teams, Tencent Meeting, and Feishu, showcasing its robust adaptability. © 2024 IEEE.

关键词： Video conferencing

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DPSFC: A distributed Delay-Aware Approach for Improving parallelized Service Function Chains Placement 22

DPSFC: A Distributed Delay-Aware Approach for Improving Para...

引用

22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Zhang, Mingxin Ai, Yintan Li, Hua Inner Mongolia University College of Computer Science Hohhot China

ISBN: (纸本)9798331509712

Network Function Virtualization (NFV) virtualizes traditional hardware-based Network devices and intermediate boxes into Virtual Network Functions(VNF). A series of VNF through which packets pass constitute a Service Function Chain(SFC). VNF can be placed as software on general-purpose commercial servers, greatly increasing the flexibility of network services and reducing costs. Packets need to pass through many VNFs to get from the source to the destination of an SFC. To reduce end-to-end delay caused by virtualization and chain length. SFC parallelization has been a successful solution. However, the existing parallelized SFC(PSFC) placement method does not conform to the distributed design idea of network services. In this paper, we propose a distributed delay-aware PSFC placement approach called DPSFC that all VNF in PSFC are placed on different physical nodes as much as possible and balancing link propagation delay and VNF processing delay to reduce packet deposition at the merge node of parallel modules(pm). Finally, a link packet marking method is proposed to reduce the extra link bandwidth of distributed VNF placement. The simulation results show that compared with the existing approaches, our approach can improve the request acceptance ratio of PSFC while ensuring end-to-end delay. © 2024 IEEE.

关键词： Network function virtualization

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Low-bit CUTLASS GEMM Template Auto-tuning using Neural Network 22

Low-bit CUTLASS GEMM Template Auto-tuning using Neural Netwo...

引用

22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Guo, Hong Guo, Nianhui Meinel, Christoph Yang, Haojin University of Potsdam Hasso Plattner Institute Potsdam Germany

ISBN: (纸本)9798331509712

Optimizing General Matrix Multiplication (GEMM) on GPU platforms has become increasingly important due to the scaling demands of modern deep neural network research. While substantial progress has been made in accelerating high-precision GEMM, optimizing lower-bit GEMM remains an open problem. The CUTLASS library offers highly optimized low-bit GEMM based on tensor cores, but performance varies significantly with tile and pipeline settings across different GPUs. We introduce a novel auto-tuning framework for low-bit CUTLASS GEMM that employs a neural network model to predict optimal GEMM template parameters for target GPUs. This model was trained on a synthetic dataset featuring various matrix sizes from different Ampere GPUs and evaluated on these GPUs. In the test dataset, our method achieved an accuracy of up to 92.9%. Real-time evaluations of low-bit data types on the A100 GPU demonstrated speedups of up to 2.03× for GEMM and 1.44× for the linear layer compared to the default templates. © 2024 IEEE.

关键词： Graphics processing unit

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Protein Function Prediction Based on the Pretrained Language Model ESM2 and Graph Convolutional Networks 22

Protein Function Prediction Based on the Pretrained Language...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Hou, Lijuan Qin, Hanyan Zhang, Xiankun Zhang, Yiying Tianjin University of Science and Technology College of Artificial Intelligence Tianjin China

ISBN: (纸本)9798331509712

Understanding protein function is crucial for comprehending life at the molecular level. Currently, less than 0.1% of proteins having experimental GO annotations. Traditional experimental methods are time-consuming and expensive. To narrow this gap, employing accurate and efficient computational methods can fill the void in automated protein function prediction (AFP). We have developed a new method for predicting protein function using sequence and predicted structural information. We use the large-scale pretrained language model ESM2 to pretrain protein sequences and an encoder to capture contextual information. Using the 3D structural data of proteins generated by AlphaFold2, combined with the sequence, as input for the Graph Convolutional Neural Network, to infer the probabilities of Gene Ontology (GO) annotations for the proteins. Compared to earlier methods, our model achieves better performance. Evaluations on the human dataset show AUPR improvements of 9%, 9.4%, and 20.7% in the BP, MF, and CC branches, respectively, demonstrating that our model is an effective tool for predicting protein function. © 2024 IEEE.

关键词： Gene Ontology

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CODC-pyParaQC: A design and implementation of parallel quality control for ocean observation big data 22

CODC-pyParaQC: A design and implementation of parallel quali...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Yuan, Huifeng Li, Tianyan Jin, Zhong Cheng, Lijing Tan, Zhetao Zhang, Bin Wang, Yanjun Computer Internet Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Institute of Atmospheric Physics Beijing China Chinese Academy of Sciences Institute of Oceanography Qingdao China

ISBN: (纸本)9798331509712

High-quality ocean observation is essential for research and applications in ocean exploration and climate change. With moving into the era of big data in recent years, it becomes crucial to process these massive raw observations accurately and efficiently. This paper addressed issues encountered in processing ocean big data within traditional delayed-mode quality control systems, including substantial serial I/O workloads and frequent context switching. A parallel quality control scheme named CODC-pyParaQC was proposed by constructing computing process groups. It retains the advantages of the existed delayed-mode quality control system (e.g. CODC-QC) while improving the efficiency of the quality control procedure, solving the feasibility of a large-scale parallel computation of the quality control scheme and realizing the (near) real-time quality control of massive ocean observation profiles. The results showed that the efficiency of single-node quality control has been improved by about 10 times. Leveraging the computing power of supercomputers and employing multi process groups for cross-node parallel computation, we have developed a fast and efficient (near) real-time quality control procedure. This system processed approximately 22,548,733 temperature profiles from the world ocean database (1940-2023) in about 6.5 hours. Our new quality control scheme can ensure the computing capability necessary for establishing a high-quality ocean observation profile database. © 2024 IEEE.

关键词： Efficiency

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Robot Vision-Based Autonomous Navigation Method Using Sim2Real Domain Adaptation 22

Robot Vision-Based Autonomous Navigation Method Using Sim2Re...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Zhang, Mengjiao Duan, Shihong Xu, Cheng Dept. Computer Science Beijing China

ISBN: (纸本)9798331509712

Vision-based autonomous navigation is pivotal in mobile robot technology, involving autonomous movement, path planning, obstacle avoidance, and target reaching in unknown environments. Reinforcement Learning (RL) offers a trial-and-error-driven learning method for autonomous navigation. However, training RL models directly on physical robots is time-intensive and poses potential risks. Simulators accelerate RL training efficiency and reduce costs, but they typically provide only approximate models of robot dynamics and their interactions with the environment. This leads to a simulation-to-reality gap (Sim2Real Gap), where strategies trained in simulation under-perform in real-world applications, sometimes resulting in task failures. To address the issue of reducing the Sim2Real Gap in visual sampling training, this paper establishes a bridging plugin between the simulator and ROS, enabling the subscription to interaction data between the ROS-based robot and its real-world environment. We propose a Sim2Real domain adaptation method based on CycleGAN, which generates effective visual observations for autonomous navigation learning. The experimental results demonstrate that this domain adaptation-based method, by utilizing minimal real-world data, significantly reduces the Sim2Real Gap compared to approaches relying solely on simulation data, achieving a performance improvement of 62.38%. © 2024 IEEE.

关键词： Robot vision

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parallel Assembly Sequence Planning Based on Sparrow Search Algorithms 22

Parallel Assembly Sequence Planning Based on Sparrow Search ...

引用

22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： He, Haichen Liu, Weibin Zhang, Yinan Song, Shasha Cheng, Yue Liu, Ziheng Xing, Weiwei Beijing Jiaotong University School of Computer Science and Technology China Crrc Information Technology Co. Ltd. China Beijing Jiaotong University School of Software Engineering China

ISBN: (纸本)9798331509712

Assembly sequence planning (ASP) is the most important process in the product lifecycle. This paper aims to propose a parallel assembly sequence planning method based on sparrow search algorithm (SSA) to improve the assembly quality and efficiency. The proposed method divides the problem into assembly unit division (AUD) part and ASP part. In AUD part, we use the markov clustering algorithm (MCA) to solve the AUD problem. In ASP part, the ASP evaluation system is designed for the assembly part sequence planning (APSP) problem and assembly unit sequence planning (AUSP) problem, and the discrete artificial sparrow search algorithm (DASSA) is proposed for solving the discrete APSP problem. The results of comparison experiment verify the effectiveness of the proposed method. Compared with genetic algorithm (GA) and particle swarm optimization (PSO), the proposed DASSA gets the optimal results and fastest convergence. The method proposed solves ASP problem to efficiently perform parallel assembly sequence planning and reduce production time, making it cost-effective in intelligent manufacturing. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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DMSA: Decentralized and Multi-keyword Selective Data Sharing and Acquisition 22

DMSA: Decentralized and Multi-keyword Selective Data Sharing...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Lin, Moheng Shi, Peichang Fu, Xiang Jiang, Feng Yi, Guodong National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Changsha410073 China Xiangjiang Lab Changsha410073 China

ISBN: (纸本)9798331509712

Blockchain technology has been extensively uti-lized in decentralized data-sharing applications, with the immutability of blockchain providing a witness for the circulation of data. However, current blockchain data-sharing solutions still fail to address the simultaneous screening needs of both the sender and receiver with multi-keywords. Without the capability to support bilateral simultaneous filtering, the disclosure of reasons for matching failures could inadvertently expose sensitive user data. Therefore, the challenge lies in enabling ciphertexts with multiple keywords and receivers with multiple interests to achieve mutual and simultaneous matching. Based on the technical foundations of SE (Searchable Encryption), MABE (Multi-Attribute Based Encryption), and polynomial fitting, this paper proposes a scheme called DMSA (Decentralized and Multi-keyword selective Sharing and selective Acquisition). This scheme can satisfy soundness, enabling ciphertexts carrying multiple keywords and receivers representing multiple interests to match each other simultaneously. We conducted a security analysis that confirms the security of DMSA against chosen-plaintext attacks. Our experimental results demonstrate a significant efficiency improvement, with a 67% increase over single-keyword data-sharing schemes and a 16% enhancement compared to the existing multi-keyword data-sharing solution. © 2024 IEEE.

关键词： Ciphertext

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AIO: Automating I/O Optimization Pipeline for Data-Intensive applications in HPC 22

AIO: Automating I/O Optimization Pipeline for Data-Intensive...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Wang, Wanxin Wu, Huijun Yang, Lihua Liu, Zhangyu Zheng, Bin Changsha University of Science and Technology School of Computer and Communication Engineering China National University of Defense Technology College of Computer Science and Technology China

ISBN: (纸本)9798331509712

High-performance computing (HPC) systems has entered the exascale era, but I/O performance has lagged behind due to storage hardware limitations, creating a "storage wall effect"that hinders HPC systems full potential. Modern HPC storage systems employ a layered storage architecture with local node storage, burst buffers, and global parallel file systems to improve I/O performance. However, application workloads vary, and fast storage layers may not always outperform global parallel file systems, while a static storage software stack configuration may not achieve optimal performance across different applications. Traditional I/O optimization is time-consuming, laborintensive, and prone to human error. To address this issue, this paper proposes AIO, a data-driven I/O optimization method. AIO automatically selects the optimal storage hierarchy and software stack based on the task's I/O patterns, and further enhances performance by identifying and applying the most efficient configuration settings. We evaluated AIO with eight applications in a cluster that features shared burst buffers, local burst buffers, and a global shared file system. The experiments demonstrate that AIO effectively optimizes I/O performance, achieving an overall speedup of 2.74. © 2024 IEEE.

关键词： Buffer storage

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Scheduling with Many Shared Resources 37

Scheduling with Many Shared Resources

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37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Deppert, Max A. Jansen, Klaus Maack, Marten Pukrop, Simon Rau, Malin Univ Kiel Dept Comp Sci Kiel Germany Paderborn Univ Paderborn Heinz Nixdorf Inst Paderborn Germany Paderborn Univ Dept Comp Sci Paderborn Germany

ISBN: (纸本)9798350337662

Consider the many shared resources scheduling problem where jobs have to be scheduled on identical parallel machines with the goal of minimizing the makespan. However, each job needs exactly one additional shared resource in order to be executed and hence prevents the execution of jobs that need the same resource while being processed. Previously, an approximation ratio of asymptotically 2 was the best known result for this problem. Furthermore, a 6/5-approximation for the case with only two machines was known as well as a PTAS for the case with a constant number of machines. We present a simple and fast 5/3-approximation and a much more involved but still reasonable 1.5-approximation. Furthermore, we provide a PTAS for the case with only a constant number of machines, which is arguably simpler and faster than the previously known one, as well as a PTAS with resource augmentation for the general case. The approximation schemes make use of the N-fold integer programming machinery, which has found more and more applications in the field of scheduling recently. It is plausible that the latter results can be improved and extended to more general cases. Lastly, we give an inapproximability result for the natural problem extension where each job may need up to a constant number of different resources, namely 3, ruling out better than 5/4 approximations for that case.

关键词： Scheduling Approximation parallel Identical Machines Resource Constraints Conflicts

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