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Joint Location Deployment, Offloading and Resource Allocation in Multi-UAV Collaborative Edge computing Networks 22

Joint Location Deployment, Offloading and Resource Allocatio...

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

作者： Xu, Mingyang Chen, Xin Jiao, Libo Zhao, Long Fan, Xiaoya Beijing Information Science & Technology University School of Computer Science Beijing China

ISBN: (纸本)9798331509712

Unmanned aerial vehicles (UAVs) play a crucial role in mobile edge computing (MEC). On one hand, UAVs can serve as relay nodes, being flexibly deployed in various complex areas to provide network coverage services for users in remote regions. On the other hand, UAVs can carry edge servers, enabling them to approach terminal devices more conveniently and enhance the efficiency of task computation. However, in the face of a large number of computing tasks, the load balancing, network performance and computational resource management of the network are facing serious challenges due to the differences in task density caused by the irregular movement of ground users (GUs), as well as the limitations of the UAV's computational capability and coverage. In order to address the above challenges, in this paper, We propose a multi-UAV collaborative MEC system. Our goal is to offload tasks as efficiently as possible with full and reasonable utilisation of computational resources, and to evenly distribute computational resources to meet the needs of varying levels of task intensity in a region through rational deployment of UAV locations and collaboration among UAVs. Specifically, in our proposed system framework, GUs can offload tasks to their associated UAVs and further decide whether or not to offload tasks to collaborative UAV or remote Base station (BS) based on the load of computational resources. We study the problem of location deployment of multiple UAVs for better offloading and resource allocation to tasks. Then, we model the task offloading and resource allocation process as a Markov decision process (MDP) focusing on minimising the average user cost of the system and propose a deep reinforcement learning (DRL)-based multi-uav collaborative computation offloading and resource allocation (DMCOA) algorithm. It has been shown through extensive simulation experiments that the algorithm can effectively reduce the average user cost of the MEC system. © 2024 IEEE.

关键词： Computation offloading

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Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation 36

Distributing Deep Learning Hyperparameter Tuning for 3D Medi...

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

作者： Berral, Josep Ll Aranda, Oriol Luis Dominguez, Juan Torres, Jordi Univ Politecn Cataluna Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781665497473

Most research on novel techniques for 3D Medical Image Segmentation (MIS) is currently done using Deep Learning with GPU accelerators. The principal challenge of such technique is that a single input can easily cope computing resources, and require prohibitive amounts of time to be processed. Distribution of deep learning and scalability over computing devices is an actual need for progressing on such research field. Conventional distribution of neural networks consist in "data parallelism", where data is scattered over resources (e.g., GPUs) to parallelize the training of the model. However, "experiment parallelism" is also an option, where different training processes (i.e., on a hyper-parameter search) are parallelized across resources. While the first option is much more common on 3D image segmentation, the second provides a pipeline design with less dependence among parallelized processes, allowing overhead reduction and more potential scalability. In this work we present a design for distributed deep learning training pipelines, focusing on multinode and multi-GPU environments, where the two different distribution approaches are deployed and benchmarked. We take as proof of concept the 3D U-Net architecture, using the MSD Brain Tumor Segmentation dataset, a state-of-art problem in medical image segmentation with high computing and space requirements. Using the BSC MareNostrum supercomputer as benchmarking environment, we use TensorFlow and Ray as neural network training and experiment distribution platforms. We evaluate the experiment speed-up when parallelizing, showing the potential for scaling out on GPUs and nodes. Also comparing the different parallelism techniques, showing how experiment distribution leverages better such resources through scaling, e.g. by a speed-up factor from x12 to x14 using 32 GPUs. Finally, we provide the implementation of the design open to the community, and the non-trivial steps and methodology for adapting and deploying a MIS c

关键词： distributed Deep Learning distributed computing GPU parallelism Scalability

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Breast Cancer Classification Using DDPM and Holographic Guidance Diffusion Framework 22

Breast Cancer Classification Using DDPM and Holographic Guid...

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

作者： You, Yuxin Gan, Hong-Seng Shimizu, Akinobu Ramlee, Muhammad Hanif Basarudin, Hafiz Vu, Viet-Vu Xi'an Jiaotong - Liverpool University School of Ai and Advanced Computing Suzhou China Tokyo University of Agriculture and Technology Institute of Engineering Tokyo Japan Universiti Teknologi Malaysia Department of Biomedical Engineering and Health Sciences Johor Bahru Malaysia Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Engineering and Science Kajang Malaysia Cmc University Faculty of Information Technology and Communications Hanoi Viet Nam

ISBN: (纸本)9798331509712

Breast cancer is a serious health threat to women. The early detection of breast cancer is crucial to improving survival rates and reducing the difficulty of treatment. In recent years, denoising diffusion probability models (DDPM) have performed well in image generation, but their application to breast cancer classification remains unknown. Additionally, conditional priors are often the most important source of information for diffusion models to solve problems, and fuzzy lesions or high-noise images pose a significant challenge for the model to extract accurate prior information. To address these issues, we propose a new classification model based on DDPM and combine it with the holographic guided diffusion framework (HGDF) to remove noise from images through specific guidance and make full use of HPC resources to improve the classification performance of breast cancer images. Results from the experiment indicate that the model has excellent accuracy in ultrasound images (BUS dataset) and mammography images (MAMMO dataset), with accuracy of 86.17 % ± 1.74% and 94.32% ± 2.31%, respectively. © 2024 IEEE.

关键词： Lung cancer

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DNS Covert Channel Attack Detection Based on Spatio-Temporal Feature Fusion 22

DNS Covert Channel Attack Detection Based on Spatio-Temporal...

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

作者： You, Wei Zhao, Chunlei Shan, Chao Dong, Guozhong Yu, Jie Guan, Yong Tianjin University of Technology Tianjin Key Laboratory of Intelligent Computing and Software New Technology Tianjin China Coordination Center of China Hubei Branch of National Computer Network Emergency Response Technical Team HuBei China Pengcheng Laboratory Department of New Networks Shenzhen China

ISBN: (纸本)9798331509712

An increasing number of Advanced Persistent Threat (APT) organizations are utilizing DNS (Domain Name System) covert channels to evade network intrusion detection systems and establish private authoritative servers for remote attacks. However, current detection models for DNS covert channels often have poor performance and limited generalization ability. To address these challenges, we propose a DNS covert channel attack detection method based on spatiotemporal feature fusion. This method combines the Interaction timing sequence features of DNS traffic with the spatial statistical features of domain names, and constructs a detection model called ConvSLSTM, which integrates a Convolutional Neural Network (CNN) with a stacked Long Short Term Memory (LSTM) network. CNN extracts local spatial features from traffic, while stacked LSTM captures Interaction timing sequence features, significantly improving detection accuracy. This article uses three typical public datasets for experiments, which contain a total of 440000 DNS covert channel attack data. Comparative experiments show that our method is significantly superior to existing methods and exhibits stronger generalization ability in detecting DNS covert channel attacks constructed by unknown tools. © 2024 IEEE.

关键词： Network intrusion

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parallel Algorithms for Adding a Collection of Sparse Matrices 36

Parallel Algorithms for Adding a Collection of Sparse Matric...

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

作者： Hussain, Md Taufique Abhishek, Guttu Sai Buluc, Aydin Azad, Ariful Indiana Univ Bloomington IN 47405 USA Indian Inst Technol Mumbai Maharashtra India Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781665497473

We develop a family of parallel algorithms for the SpKAdd operation that adds a collection of k sparse matrices. SpKAdd is a much needed operation in many applications including distributed memory sparse matrix-matrix multiplication (SpGEMM), streaming accumulations of graphs, and algorithmic sparsification of the gradient updates in deep learning. While adding two sparse matrices is a common operation in Matlab, Python, Intel MKL, and various GraphBLAS libraries, these implementations do not perform well when adding a large collection of sparse matrices. We develop a series of algorithms using tree merging, heap, sparse accumulator, hash table, and sliding hash table data structures. Among them, hash-based algorithms attain the theoretical lower bounds both on the computational and I/O complexities and perform the best in practice. The newly-developed hash SpKAdd makes the computation of a distributed-memory SpGEMM algorithm at least 2x faster than previous state-of-the-art algorithms.

关键词： Deep learning distributed processing Instruction sets Merging Memory management Data structures Libraries

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Depression Detection with EEG Based on Mutual Information Regularization 22

Depression Detection with EEG Based on Mutual Information Re...

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

作者： Lin, Haoyu Ma, Tianyuan Zhao, Chen Qi, Jun Zhang, Tingting Kong, Xiangzeng University of New South Wales School of Computer Science and Engineering Sydney Australia Fujian Agriculture and Forestry University School of Future Technology Fuzhou China Jiaotong-Liverpool University Department of Computing Xi'an Suzhou China

ISBN: (纸本)9798331509712

Depression is a kind of mental illness that is harmful to the development of society. Electroencephalography (EEG) is a promising tool in the area of auxiliary diagnosing diseases. In this paper, we develop a mutual information-based least absolute shrinkage and selection operator (MI-LASSO) model to learn representative features from the power spectral density (PSD) ratio extracted from data. Specifically, MI-LASSO adds an adaptive weight based on mutual information to LASSO, which can discriminate weights of different features. Following the feature selection accomplished by MI-LASSO, the feature set is input into the classifier. We design a stacking ensemble classifier composed of support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K-nearest neighbor (KNN). Compared to independent classifiers, stacking has a stronger ability to recognize depression. The proposed framework is validated on the open datasets: MODMA and the dataset from Hospital Universiti Sains Malaysia (HUSM). The best classification accuracy on MODMA achieved 99.025%. The best classification accuracy on the second dataset achieved 99.06%. The results indicate that our framework outperforms other EEG-based methods in the identification of depression. We conducted several experiments whose results demonstrate our framework can effectively assist in the diagnosis of depression based on EEG. © 2024 IEEE.

关键词： Support vector machines

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Scalable Data parallel distributed Training for Graph Neural Networks 36

Scalable Data Parallel Distributed Training for Graph Neural...

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

作者： Koyama, Sohei Tatebe, Osamu Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9781665497473

Graph neural networks (GNNs) operate on data represented as graphs, and are useful for a wide variety of tasks from chemical reaction and protein structure prediction to content recommendation systems. However, training for large graphs and improving training performance remain significant challenges. Existing distributed training systems partition a graph among all compute nodes to train for large graphs;however, this results in a communication overhead to degrade training performance. In this study, to solve these two problems, we propose a scalable data-parallel distributed GNN training system designed to partition a graph redundantly. It is implemented using remote direct memory access (RDMA) and nonblocking active messages to efficiently utilize network performance and hide communication overhead by overlapping with the training computation. Experimental results are presented to show the strong scalability of the proposed approach, which achieved parallel efficiencies of 0.93 using eight compute nodes for the ogbn-products dataset in the Open Graph Benchmark (OGB) and 0.95 based on two compute nodes using 32 compute nodes for the ogbn-papersl00M dataset. The proposed system exhibited a training performance 18.9% better than the state-of-the-art DistDGL, even with only a single compute node. The results demonstrate that the proposed approach may be considered a promising method to achieve scalable training performance for large graphs.

关键词： Graph Neural Networks distributed training High Performance computing Deep Graph Library

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LCOC: A Low-cost Optimizing Compiler for Large Scale Quantum Programs Towards Realistic Hardware 22

LCOC: A Low-cost Optimizing Compiler for Large Scale Quantum...

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

作者： Zhu, Yu Xu, Jinchen Mu, Qing Yang, Hongru Ding, Xiaodong Du, Qiming Shan, Zheng Laboratory for Advanced Computing and Intelligence Engineering Henan China

ISBN: (纸本)9798331509712

Quantum compilers play an essential role in the translation of quantum algorithms on quantum hardware. As the size of quantum programs increases, the problem of extreme compilation overhead becomes apparent. To address this problem, compilation frameworks with high-level intermediate representations such as QCOR have been introduced. However, these frameworks still face significant overhead from qubit mapping and optimizing sequence redundancy, resulting in challenges when compiling circuits to realistic hardware. In our paper, a low-cost optimizing compiler (LCOC) is proposed, which addresses the overhead in qubit mapping and enhanced the optimization capabilities. A qubit mapping optimizer based on the LLVM Polly is introduced in LCOC, which implements an efficient mapping of quantum programs containing specific loop types. While achieving low-cost mapping, it considerably reduces the depth of the circuit. We also implement an optimization sequence generator that leverages the modularity of LLVM and iterative compilation to provide LCOC with a more flexible and efficient optimization sequence. We conduct experiments with 100+ qubit circuits. Experiments show that LCOC is up to 1540× faster compared to Qiskit and 74× faster compared to QCOR in compile time. The average speedup relative to Qiskit and QCOR are 194× and 8.64×, respectively. In addition, it can effectively reduce the circuit depth by 50.32% compared to Qiskit and 83.5% compared to QCOR on average. © 2024 IEEE.

关键词： Qubits

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Accelerating Encrypted computing on Intel GPUs 36

Accelerating Encrypted Computing on Intel GPUs

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

作者： Zhai, Yujia Ibrahim, Mohannad Qiu, Yiqin Boemer, Fabian Chen, Zizhong Titov, Alexey Lyashevsky, Alexander Univ Calif Riverside Riverside CA 92521 USA North Carolina State Univ Raleigh NC USA Intel Corp Santa Clara CA USA

ISBN: (纸本)9781665481069

Homomorphic Encryption (HE) is an emerging encryption scheme that allows computations to be performed directly on encrypted messages. This property provides promising applications such as privacy-preserving deep learning and cloud computing. Prior works have been proposed to enable practical privacy-preserving applications with architectural-aware optimizations on CPUs, CUDA-enabled GPUs and FPGAs. However, there is no systematic optimization for the whole HE pipeline on Intel GPUs. In this paper, we present the first-ever SYCL-based GPU backend for Microsoft SEAL APIs. We perform optimizations from instruction level, algorithmic level and application level to accelerate our HE library based on the Cheon, Kim, Kim and Song (CKKS) scheme on Intel GPUs. The performance is validated on two latest Intel GPUs. Experimental results show that our staged optimizations together with optimizations including low-level optimizations and kernel fusion accelerate the Number Theoretic Transform (NTT), a key algorithm for HE, by up to 9.93X compared with the naive GPU baseline. The roofline analysis confirms that our optimized NTT reaches 79.8% and 85.7% of the peak performance on two GPU devices. Through the highly optimized NTT and the assembly-level optimization, we obtain 2.32X - 3.05X acceleration for HE evaluation routines. In addition, our all-together systematic optimizations improve the performance of encrypted element-wise polynomial matrix multiplication application by up to 3.11X.

关键词： Homomorphic Encryption Number Theoretic Transform Intel GPU CKKS Privacy-Preserving computing

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Efficient Erasure-Coded Data Recovery Based on Machine Learning With a Low Level of Storage Overhead 22

Efficient Erasure-Coded Data Recovery Based on Machine Learn...

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

作者： Zhao, Xiaobo Wei, Bing Luo, Ning Chen, Qian Wu, Yi Zhang, Shudong Zhou, Lijuan Hainan University School of Cyberspace Security Haikou China Hainan University School of International Communication and Arts Haikou China

ISBN: (纸本)9798331509712

distributed storage systems typically use erasure codes for fault tolerance to reduce storage overhead. However, the data repair process in erasure-coded systems can generate heavy I/O overhead. Existing methods typically increase redundancy to improve repair speed, but this approach results in substantial storage overhead. To enhance repair speed while reducing storage costs, this paper proposes a Machine Learning-based Adaptive Recovery (MLAR) method. Given an application's access patterns for a file, MLAR uses an adaptive encoding model to calculate the optimal code for each file. When applying fault tolerance with the optimal code, lower-redundancy codes can achieve faster repair speeds than higher-redundancy codes. MLAR employs machine learning to predict file access patterns. Experimental results replaying real-world I/O workloads show that, MLAR reduces storage overhead by 12.8% and recovery time by 23.7%, compared to state-of-the-art methods. © 2024 IEEE.

关键词： Redundancy

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