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共 14644 条记录，以下是171-180 订阅

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distributed Exact Structural Clustering on Large Graph 28

Distributed Exact Structural Clustering on Large Graph

引用

IEEE 28th international conference on parallel and distributed systems (IEEE ICPADS)

作者： Zhou, Jinyu Rong, Chuitian Liu, Ding Chai, Zhengyi Tiangong Univ Tianjin Peoples R China

ISBN: (纸本)9781665473156

Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for Networks) is a well-studied graph clustering algorithm that has been widely applied over the years. However, the processing time cost of sequential SCAN and its variants cannot be tolerable on large graphs. The existing parallel variants of SCAN are focusing on fully utilizing the computing capacity of multi-core computer architectures and inventing sophisticated optimization techniques on single computing node. As the objects and their relationships in cyberspace are varying over time, the scale of graph data is increasing with high rate. The graph clustering algorithms on single node are facing challenges from limited computing resources, such as computing performance, memory size and storage volume. The distributed processing algorithm is called for processing large graphs. This work presents a distributed structural graph clustering algorithm using Spark. Furthermore, the edge pruning technique and adaptive checking are optimized to improve clustering efficiency. And the label propagation clustering is simplified to reduce the communication cost in the distributed clustering iterations. It also conduct extensive experiments on real-world datasets to testify the efficiency and scalability of the distributed algorithm. Experimental results show that efficient clustering performance can be achieved and it scales well under different settings.

关键词： Structural Clustering distributed Computing Spark

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53rd international conference on parallel Processing, ICPP 2024 - Workshops Proceedings

53rd International Conference on Parallel Processing, ICPP 2...

引用

53rd international conference on parallel Processing, ICPP 2024

ISBN: (纸本)9798400718021

The proceedings contain 19 papers. The topics discussed include: structures and techniques for streaming dynamic graph processing on decentralized message-driven systems;interference-aware function inlining for code size reduction;the rewriting of DataRaceBench benchmark for OpenCL program validations;support post quantum cryptography with SIMD everywhere on RISC-V architectures;substitution of kernel functions based on pattern matching on schedule trees;fusing depthwise and pointwise convolutions for efficient inference on GPUs;design of a decentralized Web3 access interface;a distributed particle swarm optimization algorithm based on Apache spark for asynchronous parallel training of deep neural networks;and graph federated learning with center moment constraints for node classification.

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The Logarithmic Random Bidding for the parallel Roulette Wheel Selection with Precise Probabilities

The Logarithmic Random Bidding for the Parallel Roulette Whe...

引用

1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Nakano, Koji Hiroshima Univ Grad Sch Adv Sci & Engn Kagamiyama 1-4-1 Higashihiroshima 7398527 Japan

ISBN: (纸本)9798350364613;9798350364606

The roulette wheel selection is a critical process in heuristic algorithms, enabling the probabilistic choice of items based on assigned fitness values. It selects an item with a probability proportional to its fitness value. This technique is commonly employed in ant-colony algorithms to randomly determine the next city to visit when solving the traveling salesman problem. Our study focuses on parallel algorithms designed to select one of multiple processors, each associated with fitness values, using random wheel selection. We propose a novel approach called logarithmic random bidding, which achieves an expected runtime logarithmic to the number of processors with non-zero fitness values, using the CRCW-PRAM model with a shared memory of constant size. Notably, the logarithmic random bidding technique demonstrates efficient performance, particularly in scenarios where only a few processors are assigned non-zero fitness values.

关键词： roulette wheel selection fitness proportionate selection ant colony optimization traveling salesman problem parallel heuristic algorithms

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CtXnL: A Software-Hardware Co-designed Solution for Efficient CXL-Based Transaction Processing 25

CtXnL: A Software-Hardware Co-designed Solution for Efficien...

引用

30th international conference on Architectural Support for Programming Languages and Operating systems-ASPLOS

作者： Wang, Zhao Chen, Yiqi Li, Cong Guan, Yijin Niu, Dimin Guan, Tianchan Du, Zhaoyang Wei, Xingda Sun, Guangyu Peking Univ Sch Integrated Circuits Sch Comp Sci Beijing Peoples R China Peking Univ Sch Integrated Circuits Beijing Peoples R China Alibaba Grp DAMO Acad Hupan Lab Hangzhou Peoples R China Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst SEIEE Shanghai Peoples R China Peking Univ Beijing Adv Innovat Ctr Integrated Circuits Beijing Peoples R China

ISBN: (纸本)9798400710797

Transaction processing systems are the crux for modern datacenter applications, yet current multi-node systems are slow due to network overheads. This paper advocates for Compute Express Link (CXL) as a network alternative, which enables low-latency and cache-coherent shared memory accesses. However, directly adopting standard CXL primitives leads to performance degradation due to the high cost of maintaining cross-node cache coherence. To address the CXL challenges, this paper introduces CtXnL, a software-hardware co-designed system that implements a novel hybrid coherence primitive tailored to the loosely coherent nature of transactional data. The core innovation of CtXnL is empowering transaction system developers with the ability to selectively achieve data coherence. Our evaluations on OLTP workloads demonstrate that CtXnL enhances performance, outperforming current network-based systems and achieves up to 2.08x greater throughput than vanilla CXL memory sharing architectures across universal transaction processing policies.

关键词： Compute Express Link distributed transaction processing systems cache coherence

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Shared-Memory parallel Dynamic Louvain Algorithm for Community Detection

Shared-Memory Parallel Dynamic Louvain Algorithm for Communi...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Sahu, Subhajit Kothapalli, Kishore Banerjee, Dip Sankar Int Inst Informat Technol Hyderabad Hyderabad 500032 India Indian Inst Tehnol Jodhpur Surpura Bypass Rd Karwar 342030 Rajasthan India

ISBN: (纸本)9798350364613;9798350364606

Community detection refers to the identification of coherent partitions in networks. In this poster, we present a parallel dynamic Louvain algorithm that finds conummities in rapidly evolving graphs. Given a batch update of edge deletions or insertions, our algorithm identifies an approximate set of affected vertices in the graph with minimal overhead and updates the community membership of each vertex. This process repeats until convergence. Our approach achieves a mean speedup of 7.3 x, compared to our parallel and optimized implementation of Delta-screening combined with Louvain, a recently proposed stateof-the-art approach.

关键词： Population dynamics

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Concerto: Automatic Communication Optimization and Scheduling for Large-Scale Deep Learning 25

Concerto: Automatic Communication Optimization and Schedulin...

引用

30th international conference on Architectural Support for Programming Languages and Operating systems-ASPLOS

作者： Cheng, Shenggan Lin, Shengjie Diao, Lansong Wu, Hao Wang, Siyu Si, Chang Liu, Ziming Zhao, Xuanlei Du, Jiangsu Lin, Wei You, Yang Natl Univ Singapore Singapore Singapore Georgia Inst Technol Atlanta GA USA Alibaba Grp Beijing Peoples R China George Mason Univ Fairfax VA USA Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9798400706981

With the exponential growth of deep learning (DL), there arises an escalating need for scalability. Despite significant advancements in communication hardware capabilities, the time consumed by communication remains a bottleneck during training. The existing various optimizations are coupled within parallel systems to implement specific computation-communication overlap. These approaches pose challenges in terms of performance, programmability, and generality. In this paper, we introduce Concerto, a compiler framework designed to address these challenges by automatically optimizing and scheduling communication. We formulate the scheduling problem as a resource-constrained project scheduling problem and use off-the-shelf solver to get the near-optimal scheduling. And use auto-decomposition to create overlap opportunity for critical (synchronous) communication. Our evaluation shows Concerto can match or outperform state-of-the-art parallel frameworks, including Megatron-LM, JAX/XLA, DeepSpeed, and Alpa, all of which include extensive hand-crafted optimization. Unlike previous works, Concerto decouples the parallel approach and communication optimization, then can generalize to a wide variety of parallelisms without manual optimization.

关键词： distributed Deep Learning Collective Communication GPUs Fine-grained Overlap

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iCheck: Leveraging RDMA and Malleability for Application-Level Checkpointing in HPC systems 28

iCheck: Leveraging RDMA and Malleability for Application-Lev...

引用

IEEE 28th international conference on parallel and distributed systems (IEEE ICPADS)

作者： John, Jophin Araya, Isaac David Nunez Gerndt, Michael Tech Univ Munich Chair Comp Architecture & Parallel Syst Munich Germany

ISBN: (纸本)9781665473156

The estimate that the mean time between failures will be in minutes in exascale supercomputers should be alarming for application developers. The inherent system's complexity, millions of components, and susceptibility to failures make checkpointing more relevant than ever. Since most high performance scientific applications contain an in-house checkpoint restart mechanism, their performance can be impacted by the contention of parallel file system resources. A shift in checkpointing strategies is needed to thwart this behavior. With iCheck, we present a novel checkpointing framework that supports malleable multilevel application-level checkpointing. We employ an RDMA enabled configurable multi-agent-based checkpoint transfer mechanism where minimal application resources are utilized for checkpointing. The high-level API of iCheck facilitates easy integration and malleability. We have added the iCheck library into the ls1 mardyn application providing performance improvement up to five thousand times over the in-house checkpointing mechanism. LULESH, Jacobi 2D heat simulation, and a synthetic application were also used for extensive analysis.

关键词： Fault Tolerance Adaptive Checkpointing RDMA Malleable Checkpointing MPI

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Optimizing Secure Software Development Lifecycle with parallel Temporal Convolutional Network for Vulnerability Detection

Optimizing Secure Software Development Lifecycle with Parall...

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2024 international conference on distributed systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Padmakala, S. Naseera, Mohammad Sudhakar, K. Ali, Hassan Mohamed Ramesh, Banoth Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Cse Chennai India Department of Cse Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

Smart contracts are one of the software application which is developed based on blockchain, are extensively used in Internet of Things, finance management, and other applications. However, development of smart contracts increases, meanwhile vulnerability attacks elated to the software also increases which occur often. To overcome this limitation, parallel Temporal Convolutional Network (PTCN) is proposed for the vulnerability detection in secure software development. By using parallel temporal convolutions, the detection model process multiple time steps at the same time, that helps to identify patterns effectively. Firstly, Smartbugs datasets are used for acquiring data regarding the vulnerable behaviors in a software development. After that these data are pre-processed by regularization method and fed to word embedding for converting text data to vector data. Finally, from these vector data, the proposed PTCN model detects the vulnerability efficiently. The experimental results of the proposed PTCN method attained accuracy of 93.26% which is higher than the existing detection methods such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： parallel temporal convolutional network regularization method secure software development smart contracts vulnerability detection

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Design and Implementation of a Workflow-Based Architecture for distributed Medical Image AI systems 20

Design and Implementation of a Workflow-Based Architecture f...

引用

20th IEEE international conference on e-Business Engineering, ICEBE 2024

作者： Ge, Yu Zhang, Qi Chen, Yibin Chao, Kuo-Ming Hu, Pan Cai, Hongming School of Software Shanghai Jiao Tong University Shanghai China University of Roehampton Computing Department London United Kingdom

ISBN: (纸本)9798350365856

Medical Image AI systems can assist doctors in making diagnoses, thereby improving diagnostic accuracy. These systems are now widely used in hospitals. However, current AI diagnostic methods typically rely on various deep learning technologies, which require substantial computational resources. When diagnostic demands surge, traditional monolithic architectures may suffer from low computational performance and queue congestion. To address these issues, this paper compares and analyzes two system architectures based on parallel computing and distributed computing. The first one is a coarse-grained multi-service instance architecture, which uses clustering to expand the system's service instances, though it still presents some challenges. The second is a fine-grained workflow-based distributed architecture, which abstracts the diagnostic process into a workflow divided into several subtasks managed and scheduled by the cluster. This architecture demonstrates advantages in several aspects. Finally, this paper implements a Medical Image AI System for pulmonary fibrosis diagnosis based on the workflow-based distributed system architecture. © 2024 IEEE.

关键词： Medical computing

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MLPs: Efficient Training of MiniGo on Large-scale Heterogeneous Computing System 28

MLPs: Efficient Training of MiniGo on Large-scale Heterogene...

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IEEE 28th international conference on parallel and distributed systems (IEEE ICPADS)

作者： Qiao, Peng He, Zhouyu Li, Rongchun Jiang, Jingfei Dou, Yong Li, Dongsheng Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

ISBN: (纸本)9781665473156

Deep Reinforcement Learning has been successfully applied in various applications and achieved impressive performance compared with previous traditional methods but suffers from high computation cost and long training time. MLPerf takes deep reinforcement learning as one of the benchmark tracks and provides a single node training version of MiniGo as a reference. A key challenge is to achieve efficient MiniGo training on a large-scale computing system. According to the training computation pattern in MiniGo and the characteristics of our large-scale heterogeneous computing system, we propose a MultiLevel parallel strategy, MLPs, including task-level parallelism between nodes, CPU-DSP heterogeneous parallelism, and DSP multi-core parallelism. The proposed method reduces the overall execution time from 43 hours to 16 hours while scaling the node size from 1067 to 4139. The scaling efficiency is 69.1%. According to our fitting method, the scaling efficiency is 46.5% when scaling to 8235 nodes. The experimental results show that the proposed method achieves the efficient training of MiniGo on the largescale heterogeneous computing system.

关键词： deep reinforcement learning deep neural networks MLPerf heterogeneous architecture large-scale parallel computing

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