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检索条件"任意字段=20th International Conference on Parallel and Distributed Computing Systems, PDCS 2007"

共 306 条记录，以下是1-10 订阅

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20th international conference on parallel and distributed computing systems, pdcs 2007

20th International Conference on Parallel and Distributed Co...

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20th international conference on parallel and distributed computing systems, pdcs 2007

ISBN: (纸本)9781604233926

the proceedings contain 52 papers. the topics discussed include: a rapid heuristic for scheduling non-preemptive dependent periodic tasks onto multiprocessor;deadline-based scheduling of divisible real-time loads;a performance-effective task scheduling algorithm for multiprocessor computing systems;grid enabling a content based image retrieval application;multi-tier data access and hierarchical memory optimization;replica traffic manager for data grids;design considerations for resilient distributed data centers;supporting geographic data in the mobile computing environment;real-time data processing based on caching algorithm in ubiquitous information middleware;and evaluation of architectural support for speech codecs application in large-scale parallel machines.

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Proceedings of the 20th IASTED international conference on parallel and distributed computing and systems, pdcs 2008

Proceedings of the 20th IASTED International Conference on P...

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20th IASTED international conference on parallel and distributed computing and systems, pdcs 2008

ISBN: (纸本)9780889867741

the proceedings contain 57 papers. the topics discussed include: logical topology design with traffic grooming for scheduled demands;the impact of multi-core architectures on task retrieval policies for volunteer computing;evaluation of CPU power control and scheduling technique on parallel computing environment;clustered assignment of CCQS in information monitoring system supporting conjunctive queries;dense skip graphs as an overlay for P2P networks;multi-connection and multi-core aware all-gather on infiniband clusters;an improved prioritization method for task scheduling in heterogeneous computing systems;thread pipelining with clustered data communication on commodity CMPS;a distributed algorithm for disjoint paths in star networks;parallel algorithms for a visual text mining platform;and a cache miss analysis for multithreaded architectures.

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12th international conference on parallel and distributed computing systems, pdcs 99 Proceedings August 18-20, 1999, FT. Lauderdale, FL: pdcs 99 Proceedings August 18-20, 1999, FT. Lauderdale, FL

12th International Conference on Parallel and Distributed Co...

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1999年

作者： Stephan Olariu J Wu

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Proceedings - 2019 20th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2019

Proceedings - 2019 20th International Conference on Parallel...

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20th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2019

ISBN: (纸本)9781728126166

the proceedings contain 94 papers. the topics discussed include: optimizing deep learning split deployment for IoT edge networks;efficient fault-tolerant syndrome measurement of quantum error-correcting codes based on 'flag';comparison of binary rain prediction on HIMAWARI using MPI and CUDA;effect of parallel processing by duplicating histogram in automatic image binarization for high-level synthesis;joint mobile data collection and energy supply scheme for rechargeable wireless sensor networks;ultra reliable communication: availability analysis in 5G cellular networks;an improved online multidimensional bin packing algorithm;and improving recommender systems accuracy in social networks using popularity.

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

20th IFIP WG 10.3 International Conference on Network and Pa...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

ISBN: (纸本)9789819628636

the proceedings contain 76 papers. the special focus in this conference is on Network and parallel computing. the topics include: AsymFB: Accelerating LLM Training through Asymmetric Model parallelism;DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Friendly Data Communication and parallelism Strategies;Diagnosability of the Lexicographic Product of Paths and Complete Bipartite Graphs Under PMC Model;DTuner: A Construction-Based Optimization Method for Dynamic Tensor Operators Accelerating;Efficient Implementation of the LOBPCG Algorithm on a CPU-GPU Cluster;HP-CSF: An GPU Optimization Method for CP Decomposition of Incomplete Tensors;JediGAN: A Fully Decentralized Training of GAN with Adaptive Discriminator Averaging and Generator Selection;optimizing Vo-Viso: A Modified Methodology to parallel computing with Isolating Data in Memristor Arrays;parallel Computation of the Combination of Two Point Operations in Conic Curves Cryptosystem over GF(2n) Using Tile Self-assembly;parallel Construction of Independent Spanning Trees on 3-ary n-cube Networks;SpecInF: Exploiting Idle GPU Resources in distributed DL Training via Speculative Inference Filling;swDarknet: A Heterogeneous parallel Deep Learning Framework Suitable for SW26010 Pro Processor;VConv: Autotiling Convolution Algorithm Based on MLIR for Multi-core Vector accelerators;ACH-Code: An Efficient Erasure Code to Reduce Average Repair Cost in Cloud Storage systems of Multiple Availability Zones;CMS: A Computility Resource Status Management and Storage Framework;fast Memory Disaggregation with SwiftSwap;HASLB: Huge Page Allocation Strategy Optimized for Load-Balance in parallel computing Programs;lightFinder: Finding Persistent Items with Small Memory;miDedup: A Restore-Friendly Deduplication Method on Docker Image Storage systems;SPLR: A Selective Packet Loss Recovery for Improved RDMA Performance;a Cluster-Based Platoon Formation Scheme for Realistic Automated Vehicle Platooning;AnaNET: Anatomical Network fo

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

20th IFIP WG 10.3 International Conference on Network and Pa...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

ISBN: (纸本)9789819628292

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Comprehensive Deadlock Prevention for GPU Collective Communication 25

Comprehensive Deadlock Prevention for GPU Collective Communi...

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20th European conference on Computer systems, EuroSys 2025, co-located 30th ACM international conference on Architectural Support for Programming Languages and Operating systems, ASPLOS 2025

作者： Pan, Lichen Liu, Juncheng Fu, Yongquan Yuan, Jinhui Zhang, Rongkai Li, Pengze Xiao, Zhen School of Computer Science Peking University China OneFlow Research China National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology China

ISBN: (纸本)9798400711961

distributed deep neural network training necessitates efficient GPU collective communications, which are inherently susceptible to deadlocks. GPU collective deadlocks arise easily in distributed deep learning applications when multiple collectives circularly wait for each other. GPU collective deadlocks pose a significant challenge to the correct functioning and efficiency of distributed deep learning, and no general effective solutions are currently available. Only in specific scenarios, ad-hoc methods, making an application invoke collectives in a consistent order across GPUs, can be used to prevent circular collective dependency and deadlocks. this paper presents DFCCL, a novel GPU collective communication library that provides a comprehensive approach for GPU collective deadlock prevention while maintaining high performance. DFCCL achieves preemption for GPU collectives at the bottom library level, effectively preventing deadlocks even if applications cause circular collective dependency. DFCCL ensures high performance with its execution and scheduling methods for collectives. Experiments show that DFCCL effectively prevents GPU collective deadlocks in various situations. Moreover, extensive evaluations demonstrate that DFCCL delivers performance comparable to or superior to NCCL, the state-of-the-art collective communication library highly optimized for NVIDIA GPUs. © 2025 Copyright held by the owner/author(s).

关键词： Deep neural networks

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JediGAN: A Fully Decentralized Training of GAN with Adaptive Discriminator Averaging and Generator Selection 20th

JediGAN: A Fully Decentralized Training of GAN with Adapti...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Han, Xiaoming Liu, Boan Cheng, Dazhao Department of Computer Science Wuhan University Wuhan China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong

ISBN: (纸本)9789819628292

Existing decentralized learning of Generative Adversarial Network (GAN) suffers from a slower convergence rate and training instability due to the changes in the gradient-sharing approach among workers. It requires more iterations to achieve convergence and deteriorates the GAN training, consequently leading to slow down training speed and accuracy degradation. We propose JediGAN, the novel distributed GAN system that achieves optimal benefits by balancing accuracy and communication overheads through adaptive scheduling strategies. JediGAN reduces overall consensus variance by incorporating adaptive global updates for the discriminators. Additionally, JediGAN effectively leverages the internal characteristics of the GAN to decouple the training processes of the discriminators and generators during the search. It employs the concept of evolutionary selection to update the generators, thereby improving training stability. through extensive experiments, we show that JediGAN outperforms state-of-the-art decentralized GAN systems, with 13.1% accuracy and 36.6% training speed improvement. © IFIP international Federation for Information Processing 2025.

关键词： Discriminators

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SpecInF: Exploiting Idle GPU Resources in distributed DL Training via Speculative Inference Filling 20th

SpecInF: Exploiting Idle GPU Resources in Distributed DL Tr...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Lv, Cunchi Shi, Xiao Liang, Dong Tan, Wenting Zhao, Xiaofang Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Nanjing China Zhongguancun Laboratory Haidian China Nanjing Institute of InforSuperbahn Nanjing China

ISBN: (纸本)9789819628292

Deep Learning (DL), especially with Large Language Models (LLMs), brings benefits to various areas. However, DL training systems usually yield prominent idling GPU resources due to many factors, such as resource allocation and collective communication. To improve GPU utilization, we present SpecInF, which adopts a Speculative Inference Filling method to exploit idle GPU resources. It collocates each primary training instance with additional inference instances on the same GPU, detects the training bubbles and adaptively fills with online or offline inference workloads. Our results show that SpecInF can effectively enhance GPU utilization under mainstream parallel training modes, delivering additional up to 14× offline inference throughputs than TGS and 67% reduction in online inference p95 latency than MPS, while guaranteeing collocated training throughput. © IFIP international Federation for Information Processing 2025.

关键词： Resource allocation

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LDChain: A Lightweight and Scalable Blockchain System for Dynamic IoT Scenarios 20th

LDChain: A Lightweight and Scalable Blockchain System for ...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Wang, Jianrong Cao, Yuxuan Hu, Dengcheng Li, Qi Li, Sen Li, Xuewei Liu, Xiulong College of Intelligence and Computing Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China School of Future Technology Tianjin University Tianjin China

ISBN: (纸本)9789819628292

As a distributed ledger, blockchain inherently guarantees data consistency. However, when applied to the Internet of things (IoT), existing solutions employing blockchain systems struggle to address the scalability demands posed by the dynamic mobility of heterogeneous devices. To this end, we propose Lightweight DAG Chain (LDChain), a lightweight and scalable blockchain system tailored for IoT scenarios. LDChain connects weak nodes and strong nodes through a hierarchical DAG consensus algorithm. For weak nodes, we introduce a transaction-based DAG consensus mechanism utilizing a snapshot synchronization strategy to cater to the dynamic nature of weak nodes and the scalability demands of the system. the integrated snapshot synchronization strategy, based on the second latest block transaction selection algorithm, reduces the storage and computational overhead for resource-restricted devices. For strong nodes, we devise a block-based DAG consensus mechanism and a repackaging strategy, which resolves transaction conflicts across regions by managing conflicting transactions through a priority queue. We implement a prototype of LDChain and conduct experiments in a network environment comprising 10 cloud servers, simulating several strong nodes and weak nodes. the experimental results show that our system achieves a throughput exceeding 7000 transactions per second (TPS) and a transaction confirmation latency of 1.3 s with 100 nodes. © IFIP international Federation for Information Processing 2025.

关键词： Scalability

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