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共 14557 条记录，以下是101-110 订阅

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A Fast Belief Propagation-Based distributed Gauss-Newton Method for Power System State Estimation 8

A Fast Belief Propagation-Based Distributed Gauss-Newton Met...

引用

8th international conference on Cloud Computing and Big Data Analytics (ICCCBDA)

作者： Guo, Peng Shi, Danni Wang, Xuan Shi, Xinghua State Grid Smart Grid Res Inst Co Ltd State Grid Lab Grid Adv Comp & Applicat Beijing 102209 Peoples R China State Grid Smart Grid Res Inst Co Ltd Zhejiang 310007 Peoples R China

ISBN: (纸本)9781665455336

State estimation is the foundation for a variety of online power system applications in energy management systems, and the stability of power systems is directly impacted by the speed with which current system states can be obtained through state estimation. This paper proposed a fast Gaussian-Newton state estimation method for power systems based on parallel belief propagation, which implements the Gaussian belief process via multi-core and multi-thread parallel computation to achieve efficient state estimation. Simulation findings on numerous IEEE-standard power systems show that the suggested technique outperforms the traditional algorithm.

关键词： Gauss-Newton state estimation factor graphs belief propagation parallel computing

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Scalable Node Embedding Algorithms using distributed Sparse Matrix Operations

Scalable Node Embedding Algorithms using Distributed Sparse ...

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

作者： Ranawaka, Isuru Azad, Ariful Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9798350364613;9798350364606

We introduce a distributed memory parallel algorithm for force-directed node embedding that places vertices of a graph into a low-dimensional vector space based on the interplay of attraction among neighboring vertices and repulsion among distant vertices. We develop our algorithms using two sparse matrix operations, SDDMM and SpMM. We propose a configurable pull -push -based communication strategy that optimizes memory usage and data transfers based on computing resources and asynchronous MPI communication to overlap communication and computation. Our algorithm scales up to 256 nodes on distributed supercomputers by surpassing the performance of state-of-the-art algorithms

关键词： Embedding SDDMM Sparse Matrix Computation SpMM

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Integration Framework for Online Thread Throttling with Thread and Page Mapping on NUMA systems

Integration Framework for Online Thread Throttling with Thre...

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

作者： Schwarzrock, Janaina Lorenzon, Arthur E. de Souza, Samuel Xavier Beck, Antonio Carlos S. Univ Fed Rio Grande do Sul Porto Alegre RS Brazil Univ Fed Rio Grande do Norte Natal RN Brazil

ISBN: (纸本)9798350364613;9798350364606

Non-Uniform Memory Access (NUMA) systems are preva-lent in HPC, where optimal thread and page placement are crucial for enhancing performance and minimizing energy us-age [1]-[3]. Moreover, considering that NUMA systems have hardware support for a large number of hardware threads and many parallel applications have limited scalability, throttling the number of active threads may bring further improvements [4]-[6]. However, the optimal configuration (thread mapping, page mapping, number of threads) for energy and performance, quantified by the Energy-Delay Product (EDP), varies with the system hardware, application, input set, and even during execution [1], [4], [6], [7]. Only online optimization approaches can easily adapt to these changes. © 2024 IEEE.

关键词： parallel applications NUMA systems dynamic concurrency throttling thread mapping page mapping

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GraphFlow: A Fast and Accurate distributed Streaming Graph Computation Model 30

GraphFlow: A Fast and Accurate Distributed Streaming Graph C...

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30th IEEE international conference on parallel and distributed systems, ICPADS 2024

作者： Liang, Zheheng Zheng, Yingying Bi, Sheng Yao, Chaosheng Wang, Jiayan Xu, Lijie Ji, Shuping Wang, Wei Duan, Shikai Information Center Guangdong Power Grid Limited Liability Company Guangzhou China Joint Laboratory on Cyberspace Security China Southern Power Grid Guangzhou China State Key Lab of Computer Science at Iscas China University of Cas Beijing China Guangzhou Power Supply Bureau Guangzhou China University of Toronto Toronto Canada Nanjing Institute of Software Technology China University of Cas Nanjing China Ant Group-Payment Business Group Hangzhou China

ISBN: (纸本)9798331515966

Streaming graph computation has been widely applied in many fields, e.g., social network analysis and online product recommendation. However, existing streaming graph computation approaches still present limitations on accuracy and efficiency. To improve the accuracy, some distributed systems use the sequential graph update method based on an incremental computation model. However, these systems cannot handle the dynamic graph update concurrently. The speculation-based parallel updating model can parallelize the graph computation, however, it is restricted due to ignoring the original messages when updating a graph. Streaming graph computation usually requires high accuracy and low latency. As such, it is challenging to utilize incremental computation while simultaneously supplying concurrent processing *** overcome these challenges, in this paper, we first analyze a number of classical graph algorithms and summarize three principles that graph algorithms should satisfy in streaming scenarios. Based on these principles, we propose GraphFlow, a streaming graph computation model. GraphFlow achieves fast and accurate computation by utilizing incremental state update and propagation. To reduce the impact of concurrent update conflicts, GraphFlow provides a fine-grained lock based parallel update strategy. We implement GraphFlow framework and evaluate its performance and concurrent update conflict probability on real-world datasets. Meanwhile, we compare GraphFlow with two existing representative graph processing systems. Experimental results show GraphFlow achieves low latency and outperforms other graph processing systems given large datasets. © 2024 IEEE.

关键词： Data flow graphs

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distributed Smoke Simulation System with Adaptive Task Segmentation 4

Distributed Smoke Simulation System with Adaptive Task Segme...

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4th international conference on Information Science, parallel and distributed systems, ISPDS 2023

作者： Hu, Jinhao Du, Jinlian Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9798350337181

The application of GPU to accelerate large-scale smoke simulation is a hot research topic in computational fluid dynamics. However, the current smoke parallel computing methods for different scale smoke flow field, there are some problems, such as the need to manually adjust some algorithms, task allocation does not consider the difference between the nodes computing power. In order to meet the adaptive requirements of smoke parallel computing, a distributed smoke simulation system with adaptive task partitioning is designed. The computing power of nodes and the transmission cost of data exchange are fully considered. The problem of boundary data synchronization is solved and the correctness of parallel computing results is ensured. © 2023 IEEE.

关键词： Computing power

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VAKY: Scheduling In-network Aggregation for distributed Deep Training Acceleration 30

VAKY: Scheduling In-network Aggregation for Distributed Deep...

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30th IEEE international conference on parallel and distributed systems, ICPADS 2024

作者： Cui, Penglai Pan, Heng Zhou, Jianer Wu, Qinghua Wang, Zhaohua Li, Zhenyu Huawei Technologies Co. Ltd. Institute of Computing Technology Beijing China Chinese Academy of Sciences Computer Network Information Center Beijing China Peng Cheng Laboratory Shenzhen China Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (纸本)9798331515966

distributed machine learning (DML) has recently experienced widespread application. A major performance bottleneck is the costly communication for gradients synchronization. Recently, researchers have explored the use of programmable switches for in-network synchronous aggregation of gradients to mitigate the communication overhead. Nevertheless, the performance of in-network synchronous aggregation is significantly impacted by the stragglers. Unfortunately, the schedulers in existing DML systems are no longer effective in dealing with stragglers because of the ignorance of the aggregation progress that is offloaded from the parameter servers to the programmable switches. To address this gap, this paper presents VAKY, an adaptive scheduler specifically designed for in-network aggregation. At the heart of VAKY is the variable K-block sync method, where the aggregators stop waiting for updates from more workers once having received updates from the fastest K workers for each block of gradients. We propose an efficient solution that can dynamically choose the optimal values of K during the training process, in order to minimize the expected training completion time. We have integrated VAKY into PyTorch, and our experiments show that compared to the state-of-the-art in-network aggregation systems, VAKY improves the aggregation throughput by up to 40% and reduces the training time by 25%. © 2024 IEEE.

关键词： Personnel training

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Enabling Dynamic Selection of Implementation Variants in Component-Based parallel Programming for Heterogeneous systems

Enabling Dynamic Selection of Implementation Variants in Com...

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29th international conference on parallel and distributed Computing (Euro-Par)

作者： Memeti, Suejb Blekinge Inst Technol Dept Comp Sci DIDA Karlskrona Sweden

ISBN: (纸本)9783031506833;9783031506840

Heterogeneous systems, consisting of CPUs and GPUs, offer the capability to address the demands of compute- and data-intensive applications. However, programming such systems is challenging, requiring knowledge of various parallel programming frameworks. This paper introduces COMPAR, a component-based parallel programming framework that enables the exposure and selection of multiple implementation variants of components at runtime. The framework leverages compiler directive-based language extensions to annotate the source code and generate the necessary glue code for the StarPU runtime system. COMPAR provides a unified view of implementation variants and allows for intelligent selection based on runtime context. Our evaluation demonstrates the effectiveness of COMPAR through benchmark applications. The proposed approach simplifies heterogeneous parallel programming and promotes code reuse while achieving optimal performance.

关键词： component-based programming implementation variant selection heterogeneous parallel computing systems source-to-source compilation performance optimization

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Accelerating distributed ML Training via Selective Synchronization 25

Accelerating Distributed ML Training via Selective Synchroni...

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25th IEEE international conference on Cluster Computing (CLUSTER)

作者： Tyagi, Sahil Swany, Martin Indiana Univ Bloomington Bloomington IN 47405 USA

ISBN: (纸本)9798350307924

In distributed training, deep neural networks (DNNs) are launched over multiple workers concurrently and aggregate their local updates on each step in bulk-synchronous parallel (BSP) training. However, BSP does not linearly scale-out due to high communication cost of aggregation. To mitigate this overhead, alternatives like Federated Averaging (FedAvg) and Stale-Synchronous parallel (SSP) either reduce synchronization frequency or eliminate it altogether, usually at the cost of lower final accuracy. In this paper, we present SelSync, a practical, low-overhead method for DNN training that dynamically chooses to incur or avoid communication at each step either by calling the aggregation op or applying local updates based on their significance. We propose various optimizations as part of SelSync to improve convergence in the context of semi-synchronous training. Our system converges to the same or better accuracy than BSP while reducing training time by up to 14x.

关键词： Cluster computing distributed systems Data-parallel training Federated learning Deep learning

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Accelerating Synchronous distributed Data parallel Training with Small Batch Sizes 29th

Accelerating Synchronous Distributed Data Parallel Training ...

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29th international conference on Database systems for Advanced Applications (DASFAA)

作者： Sun, Yushu Bi, Nifei Xu, Chen Niu, Yuean Zhou, Hongfu East China Normal Univ Shanghai Peoples R China Shanghai Engn Res Ctr Big Data Management Shanghai Peoples R China Shanghai Inst Proc Automat & Instrumentat Shanghai Peoples R China

ISBN: (纸本)9789819755684;9789819755691

Synchronous distributed data parallel (SDDP) training is widely employed in distributed deep learning systems to train DNN models on large datasets. The performance of SDDP training essentially depends on the communication overhead and the statistical efficiency. However, existing approaches only optimize either the communication overhead or the statistical efficiency to accelerate SDDP training. In this paper, we adopt the advantages of those approaches and design a new approach, namely SkipSMA, that benefits from both low communication overhead and high statistical efficiency. In particular, we exploit the skipping strategy with an adaptive interval to decrease the communication frequency, which guarantees low communication overhead. Moreover, we employ the correction technique to mitigate the divergence while keeping small batch sizes, which ensures high statistical efficiency. To demonstrate the performance of SkipSMA, we integrate it into TensorFlow. Our experiments show that SkipSMA outperforms the state-of-the-art solutions for SDDP training, e.g., 6.88x speedup over SSGD.

关键词： Deep Learning systems Data parallel Synchronous Training distributed Training

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Lightweight Streaming Graph Partitioning by Fully Utilizing Knowledge from Local View 43

Lightweight Streaming Graph Partitioning by Fully Utilizing ...

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43rd IEEE international conference on distributed Computing systems (ICDCS)

作者： Wang, Zhigang Yang, Zichao Wang, Ning Du, Yujie Nie, Jie Wei, Zhiqiang Gu, Yu Yu, Ge Ocean Univ China Qingdao Peoples R China Yantai Engn & Tech Coll Yantai Peoples R China Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350339864

Data partitioning is the most fundamental procedure before parallelizing complex analysis on very big graphs. As a classical NP-complete problem, graph partitioning usually employs offline or online/streaming heuristics to find approximately optimal solutions. However, they are either heavyweight in space and time overheads or suboptimal in quality measured by workload balance and the number of cutting edges across partitions, both of which cannot scale well with the ever-growing demands of quickly analyzing big graphs. This paper thereby proposes a new vertex partitioner for better scalability. It preserves the lightweight advantage of existing streaming heuristics, and more importantly, fully utilizes the knowledge embedded in the local view when streaming a vertex, which significantly improves the quality. We present a sliding window technique to compensate for the additional memory costs caused by knowledge utilization. Also, a parallel technique with dependency detection optimization is designed to further enhance efficiency. Experiments on a spread of real-world datasets validate that our proposals can achieve overall success in terms of partitioning quality, memory consumption, and runtime efficiency.

关键词： Streaming Graph Partitioning Local Streaming View Memory Consumption parallel Partitioning

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