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检索条件"任意字段=Distributed Memory Computing Conference"

共 8906 条记录，以下是41-50 订阅

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Optimal Dispersion in Triangular Grids: Achieving Efficiency Without Prior Knowledge 21st

Optimal Dispersion in Triangular Grids: Achieving Efficiency...

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21st International conference on distributed computing and Intelligent Technology

作者： Pandit, Himani Pandit, Supantha Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar Gujarat India

ISBN: (纸本)9783031814037;9783031814044

In the dispersion problem, a group of k <= n mobile robots, initially placed on the vertices of an anonymous graph G with n vertices, must redistribute themselves so that each vertex hosts no more than one robot. We address this challenge on an anonymous triangular grid graph, where each vertex can connect to up to six adjacent vertices. We propose a distributed deterministic algorithm that achieves dispersion on an unoriented triangular grid graph in O(root n) time, where n is the number of vertices. Each robot requires O(log n) bits of memory. The time complexity of our algorithm and the memory usage per robot are optimal. This work builds on previous studies by Kshemkalyani et al. [WALCOM 2020 [17]] and Banerjee et al. [ALGOWIN 2024 [3]]. Importantly, our algorithm terminates without requiring prior knowledge of n and resolves a question posed by Banerjee et al. [ALGOWIN 2024 [3]].

关键词： distributed algorithms Mobile robots Dispersion Triangular grid Deterministic algorithms

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HM2: Efficient Host memory Management for RDMA-Enabled distributed Systems 23

HM2: Efficient Host Memory Management for RDMA-Enabled Distr...

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32nd International Symposium on High-Performance Parallel and distributed computing (HPDC) part of the ACM Federated computing Research conference (FCRC)

作者： Tang, Chen Chu, Zhaole Jin, Peiquan Luo, Yongping Guo, Kuankuan Univ Sci Technol China Hefei Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798400701559

Remote direct memory access (RDMA) supports zero-copy networking by transferring data from clients directly to host memory, eliminating the need to copy data between clients' memory and the data buffers in the hosting server. However, the hosting server must design efficient memory management schemes to handle incoming clients' data. In this paper, we propose a high-performance host memory management scheme called HM2 for RDMA-enabled distributed systems. We present a new buffer structure for incoming data from clients. In addition, we propose efficient data processing methods to reduce network transfers between clients and servers. We conducted a preliminary experiment to evaluate HM2, and the results showed HM2 achieved higher throughput than existing schemes, including L5 and FaRM.

关键词： RDMA memory management data buffer

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DISCO: distributed Inference with Sparse Communications

DISCO: Distributed Inference with Sparse Communications

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IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Qin, Minghai Sun, Chao Hofmann, Jaco Vucinic, Dejan Western Digital Fremont CA 94539 USA

ISBN: (纸本)9798350318920;9798350318937

Deep neural networks (DNNs) have great potential to solve many real-world problems, but they usually require an extensive amount of computation and memory. It is of great difficulty to deploy a large DNN model to a single resource-limited device with small memory capacity. distributed computing is a common approach to reduce single-node memory consumption and to accelerate the inference of DNN models. In this paper, we explore the "within-layer model parallelism", which distributes the inference of each layer into multiple nodes. In this way, the memory requirement can be distributed to many nodes, making it possible to use several edge devices to infer a large DNN model. Due to the dependency within each layer, data communications between nodes during this parallel inference can be a bottleneck when the communication bandwidth is limited. We propose a framework to train DNN models for distributed Inference with Sparse Communications (DISCO). We convert the problem of selecting which subset of data to transmit between nodes into a model optimization problem, and derive models with both computation and communication reduction when each layer is inferred on multiple nodes. We show the benefit of the DISCO framework on a variety of CV tasks such as image classification, object detection, semantic segmentation, and image super resolution. The corresponding models include important DNN building blocks such as convolutions and transformers. For example, each layer of a ResNet-50 model can be distributively inferred across two nodes with 5x less data communications, almost half overall computations and less than half memory requirement for a single node, and achieve comparable accuracy to the original ResNet-50 model.

关键词： Algorithms and algorithms formulations Machine learning architectures

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Performance Optimization and Comparative Analysis of distributed Big Data Processing Framework

Performance Optimization and Comparative Analysis of Distrib...

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2024 International conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Xie, Xinyan Chongqing Three Gorges Vocational College 404155 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

The research and application of big data processing and analysis have been quite mature, but more and more fields have put forward demands for real-time analysis and rapid response of fast and massive distributed big data. distributed big data framework performance needs to be optimized. To this end, the article studies and analyzes the platform architecture and data computing model of typical batch processing technology Hadoop, memory computing technology Spark, and stream computing technology Storm, and summarizes the similarities and differences of these three big data processing technologies. Then, on the basis of studying the big data platform and traditional cardinality estimation algorithm, a HyperLogLog algorithm application model based on the streaming platform is proposed, and the cardinality calculation is performed in the Storm processing engine to achieve the performance of the distributed big data processing framework. optimization. The results show that the HyperLogLog cardinality estimation algorithm of the Storm stream computing platform can be used to optimize the performance of the distributed big data processing framework. © 2025 SPIE.

关键词： Data centers

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Out-of-memory GPU Sorting Using Asynchronous CUDA Streams 25th

Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams

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25th International conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Zomaya, Albert Y. School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819642069

Graphics Processing Units (GPUs) are widely used as powerful hardware accelerators for data-intensive tasks. However, their efficacy can be hindered by constraints in device memory and data transfer speeds via the PCI-E link. This paper explores the use of GPUs for executing scalable sorting operations that exceeds the GPU’s global memory by a factor of at least two orders of magnitude. To address this challenge, we leverage the power of NVIDIA’s asynchronous CUDA streams. Our solution enhances GPU performance by implementing a pipeline model to offload sorting operations from the Solid-State Drive (SSD) to GPU memory via the host’s main memory. This strategy maximizes the multiway concurrency level to reduce the overhead associated with data transfer within the GPU memory hierarchy. We perform extensive performance evaluations by sorting 128 billion data elements initially stored on an external NVMe SSD. The results confirm the effectiveness of our approach, showcasing a speed-up of 4.3× compared to the results for CPU-based out-of-memory sorting of skewed data based on Intel’s Threading Building Blocks (TBB) sorting algorithm. Our results include the cost of transfer times to and from the main memory, a factor often overlooked in similar studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graphics processing unit

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

Lightweight Streaming Graph Partitioning by Fully Utilizing ...

引用

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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distributed-memory Parallel Algorithms for Sparse Matrix and Sparse Tall-and-Skinny Matrix Multiplication 24

Distributed-Memory Parallel Algorithms for Sparse Matrix and...

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2024 International conference for High Performance computing, Networking, Storage and Analysis

作者： Ranawaka, Isuru Hussain, Md Taufique Block, Charles Gerogiannis, Gerasimos Torrellas, Josep Azad, Ariful Indiana Univ Bloomington IN 47405 USA Univ Illinois Champaign IL USA

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

We consider a sparse matrix-matrix multiplication (SpGEMM) setting where one matrix is square and the other is tall and skinny. This special variant, TS-SpGEMM, has important applications in multi-source breadth-first search, influence maximization, sparse graph embedding, and algebraic multi-grid solvers. Unfortunately, popular distributed algorithms like sparse SUMMA deliver suboptimal performance for TS-SpGEMM. To address this limitation, we develop a novel distributed-memory algorithm tailored for TS-SpGEMM. Our approach employs customized 1D partitioning for all matrices involved and leverages sparsity-aware tiling for efficient data transfers. In addition, it minimizes communication overhead by incorporating both local and remote computations. On average, our TS-SpGEMM algorithm attains 5x performance gains over 2D and 3D SUMMA. Furthermore, we use our algorithm to implement multi-source breadth-first search and sparse graph embedding algorithms and demonstrate their scalability up to 512 Nodes (or 65,536 cores) on NERSC Perlmutter.

关键词： Three-dimensional displays Shape Scalability Performance gain Data transfer Partitioning algorithms Sparse matrices distributed algorithms Parallel algorithms Optimization

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Research on Market Power Platform for Power Generation Based on Spark distributed Framework computing

Research on Market Power Platform for Power Generation Based...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft computing, CITSC 2025

作者： Fan, Wenjie Jia, Mingzhen Liu, Zhitong State Grid Liaoning Marketing Service Center Shenyang110006 China

ISBN: (纸本)9798331504205

The Spark distributed algorithm is a distributed computing implementation based on the map reduce algorithm, which has the advantages of Hadoop MapReduce. However, unlike MapReduce, the intermediate output and results of jobs can be saved in memory, eliminating the need to read and write HDFS. Therefore, Spark is better suited for iterative map reduce algorithms such as data mining and machine learning. Spark distributed algorithm has been widely used in the Internet, advertising, finance and other industries, but less in the power industry. At present, the research on market power risk of power generators has been very in-depth, but there is little research on simulation platform algorithms for identifying market power risk. This article proposes a market power identification platform for power generation enterprises based on the Spark distributed framework, which can effectively accelerate the calculation speed of market power indicators. Furthermore, using a certain regional power grid as a simulation example, the monopoly situation of various power generation enterprises and the entire wholesale market is analyzed, proving the effectiveness of the Spark distributed framework algorithm. As the number of grid nodes increases, the advantages of the distributed algorithm will become more prominent. © 2025 IEEE.

关键词： Market Power Platform Power Generation Spark distributed Framework computing

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NEO-DNND: Communication-Optimized distributed Nearest Neighbor Graph Construction

NEO-DNND: Communication-Optimized Distributed Nearest Neighb...

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2024 Workshops of the International conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Iwabuchi, Keita Steil, Trevor Priest, Benjamin W. Pearce, Roger Sanders, Geoffrey Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350355543

Graph-based approximate nearest neighbor algorithms have shown high neighbor structure representation quality. NN-Descent is a widely known graph-based approximate nearest neighbor (ANN) algorithm. However, graph-based approaches are memory- and *** address the drawbacks, we develop a scalable distributed NN-Descent. Our NEO-DNND (neighbor-checking efficiency optimized distributed NN-Descent) is built on top of MPI and designed to utilize network bandwidth efficiently. NEO-DNND reduces duplicate elements, increases intra-node data sharing, and leverages available DRAM to replicate data that may be sent ***-DNND showed remarkable scalability up to 256 nodes and was able to construct neighborhood graphs from billion-scale datasets. Compared to a leading shared-memory ANN library, NEO-DNND achieved competitive performance even on a single node and exhibited 41.7X better performance by scaling up to 32 nodes. Furthermore, NEO-DNND outperformed a state-of-the-art distributed NN-Descent implementation, achieving up to a 6.0X speedup. © 2024 IEEE.

关键词： approximate nearest neighbor distributed computing

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Improving the MPI Remote memory Access Model for distributed-memory Systems by Implementing One-sided Broadcast 27

Improving the MPI Remote Memory Access Model for Distributed...

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27th International conference on Soft computing and Measurements, SCM 2024

作者： Abuelsoud, Mohamed Paznikov, Aleksej A. Saint Petersburg Electrotechnical University 'LETI' Department of Computer Science and Engineering Saint Petersburg Russia

ISBN: (纸本)9798350363708

Currently, processing large volumes of expanding data efficiently and consistently is a significant challenge. Traditional distributed-memory high-performance computers (HPC) based on message-passing model struggle with inherent synchronization difficulties, limiting their ability to keep pace. Remote memory Access (RMA, also known as one-sided MPI communications) allows a process to directly read from or write to the memory of another process, bypassing the need for message exchange. Unfortunately, there is no collective operation interface in the current MPI RMA standard. However, RMA has the potential to reduce synchronization costs by enabling concurrent access to shared data structures, distributed among MPI processes' memories. Existing onesided MPI standards offer a linear interface only that hampers parallelization and far from efficient. To bridge this gap, we propose an algorithm design for efficient collective (parallelizable) operations in the RMA paradigm. Our study primarily examines the benefits of collective operations using the broadcast algorithm as an example. Our implementations surpass traditional methods, demonstrating the promising potential of this technique, as more performance tests indicate. © 2024 IEEE.

关键词： Parallel programming

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