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检索条件"任意字段=International Parallel and Distributed Processing Symposium"

共 11367 条记录，以下是81-90 订阅

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Exact Fault-Tolerant Consensus with Voting Validity 37

Exact Fault-Tolerant Consensus with Voting Validity

引用

37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Xu, Zhangchen Li, Yuetai Feng, Chenglin Zhang, Lei Univ Washington Dept Elect & Comp Engn Seattle WA 98195 USA Univ Glasgow James Watt Sch Engn Glasgow Lanark Scotland

ISBN: (纸本)9798350337662

This paper investigates the multi-valued fault-tolerant distributed consensus problem that pursues exact output. To this end, the voting validity, which requires the consensus output of non-faulty nodes to be the exact plurality of the input of non-faulty nodes, is investigated. Considering a specific distribution of nonfaulty votes, we first give the impossibility results and a tight lower bound of system tolerance achieving agreement, termination and voting validity. A practical consensus algorithm that satisfies voting validity in the Byzantine fault model is proposed subsequently. To ensure the exactness of outputs in any non-faulty vote distribution, we further propose safety-critical tolerance and a corresponding protocol that prioritizes voting validity over termination property. To refine the proposed protocols, we propose an incremental threshold algorithm that accelerates protocol operation speed. We also optimize consensus algorithms with the local broadcast model to enhance the protocol's fault tolerance ability.

关键词： Fault Tolerance distributed Algorithms Impossibility Results Voting Exact Consensus

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qTask: Task-parallel Quantum Circuit Simulation with Incrementality 37

qTask: Task-parallel Quantum Circuit Simulation with Increme...

引用

37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Huang, Tsung-Wei Univ Utah Dept Elect & Comp Engn Salt Lake City UT 84112 USA

ISBN: (纸本)9798350337662

Incremental quantum circuit simulation has emerged as an important tool for simulation-driven quantum applications, such as circuit synthesis, verification, and analysis. When a small portion of the circuit is modified, the simulator must incrementally update state amplitudes for reasonable turnaround time and productivity. However, this type of incrementality has been largely ignored by existing research. To fill this gap, we introduce a new incremental quantum circuit simulator called qTask. qTask leverages a task-parallel decomposition strategy to explore both inter- and intra-gate operation parallelisms from partitioned data blocks. Our partitioning strategy effectively narrows down incremental update to a small set of partitions affected by circuit modifiers. We have demonstrated the promising performance of qTask on QASMBench benchmarks. Compared to two state-of-the-art simulators, Qulacs and Qiskit, qTask is respectively 1.46x and 1.71x faster for full simulation and 5.77x and 9.76x faster for incremental simulation.

关键词： quantum circuit simulation task parallelism

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Lookup Parameter Optimization for Kademlia DHT Alternative in IPFS

Lookup Parameter Optimization for Kademlia DHT Alternative i...

引用

37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Kanemitsu, Hidehiro Kanai, Kenji Nakazato, Hidenori Tokyo Univ Technol Sch Comp Sci Tokyo Japan Waseda Univ Waseda Res Inst Sci & Engn Tokyo Japan Waseda Univ Sch Fundamental Sci & Engn Tokyo Japan

ISBN: (纸本)9798350311990

Peer-to-peer networks, such as IPFS, adopt the distributed hash table (DHT) to efficiently nd contents. In particular, Kademlia, which is used in IPFS, requires parameters regarding the lookup concurrency, the number of next hops, and the k-bucket size. However, such values are manually set and then the con guration is not optimal for minimizing the network latency in any network dynamics. In this paper, we present a method for automatically deriving the optimal lookup parameters for KadRTT, which is a modified version of Kademlia to improve the lookup latency. We derive the optimal values for the k-bucket size, lookup concurrency, and the number of next hops using the lookup message arrival rate, initial ID distance, and lookup iteration count. From the experimental comparisons by both a simulation and an emulation, we show that our proposal contributes to the lookup latency, and overlay hop count.

关键词： DHT distributed Hash Table Kademlia IPFS libp2p RTT

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A High Performance Algorithmic Variant of MATSim Road Traffic Simulator

A High Performance Algorithmic Variant of MATSim Road Traffi...

引用

37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Moukir, Sara Emad, Nahid Baudelocq, Stephane Univ Paris Saclay UVSQ Eiffage Energie Syst LI PaRAD & Maison Simulat Velizy Villacoublay France Univ Paris Saclay UVSQ LI PaRAD & Maison Simulat Gif Sur Yvette France Eiffage Energie Syst Velizy Villacoublay France

ISBN: (纸本)9798350311990

In this paper, we propose a new high-performance computing approach to road traffic simulation. Multi-agent road traffic simulators are the most accurate and realistic that currently exist, however they are very resource intensive and the data are massive when simulating a metropolis or a region. The main contribution of this paper is the presentation of a new concept based on the Unite and Conquer approach, allowing to set up several parallel executions with different parameters. Applied on MATSim, one of the most present multi-agent traffic simulators in the literature, it allows to reduce the number of iterations needed to converge to the optimal solution. In this paper, we show how the Unite and Conquer approach applied to MATSim brings a substantial gain in computing time and points out its potential application to other multi-agent simulators.

关键词： road traffic simulation big data analysis high performance computing complex and heterogeneous dynamic system Unite and Conquer MATSim parallel computing

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Future Computing with the Rogues Gallery

Future Computing with the Rogues Gallery

引用

37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Jezghani, Aaron Young, Jeffrey Powell, Will Rahaman, Ronald Coulter, J. Eric Georgia Inst Technol Partnership Adv Comp Env Atlanta GA 30332 USA Georgia Inst Technol Sch Comp Sci Atlanta GA USA Georgia Inst Technol Sch Comp Sci & Eng Atlanta GA USA

ISBN: (纸本)9798350311990

The Vertically Integrated Projects (VIP) Program at Georgia Tech provides a multidisciplinary research experience aimed at engaging undergraduate and graduate research students in large-scale computing research projects. Since 2019, the Future Computing with the Rogues Gallery VIP course has engaged over 75 students in research on topics related to novel architectures and "post-Moore" computing platforms built around quantum, neuromorphic, near-memory, and reconfigurable computing. One of the key takeaways from this course for the course designers has been on the correlation between these novel computing platforms and traditional skills, techniques, and tools that are used in the HPC and parallel computing arenas. We discuss these parallels as well as the impacts of this course on general student success and research outcomes.

关键词： undergraduate education parallel computing novel architecture testbeds student-oriented research

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Exploiting Sparsity in Pruned Neural Networks to Optimize Large Model Training 37

Exploiting Sparsity in Pruned Neural Networks to Optimize La...

引用

37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Singh, Siddharth Bhatele, Abhinav Univ Maryland Dept Comp Sci College Pk MD 20742 USA

ISBN: (纸本)9798350337662

parallel training of neural networks at scale is challenging due to significant overheads arising from communication. Recently, deep learning researchers have developed a variety of pruning algorithms that are capable of pruning (i.e. setting to zero) 80-90% of the parameters in a neural network to yield sparse subnetworks that equal the accuracy of the unpruned parent network. In this work, we propose a novel approach that exploits these sparse subnetworks to optimize the memory utilization and communication in two popular algorithms for parallel deep learning namely - data and inter-layer parallelism. We integrate our approach into AxoNN, a highly scalable framework for parallel deep learning that relies on data and inter-layer parallelism, and demonstrate the reduction in communication time and memory utilization. On 512 NVIDIA V100 GPUs, our optimizations reduce the memory consumption of a 2.7 billion parameter model by 74%, and the total communication time by 40%, thus providing an overall speedup of 34% over AxoNN, 32% over DeepSpeed-3D and 46% over Sputnik, a sparse matrix computation baseline.

关键词： lottery ticket hypothesis sparse computations GPUs parallel deep learning memory optimizations

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parallel COREGISTRATION ALGORITHM FOR SAR IMAGES BASED ON HADOOP

PARALLEL COREGISTRATION ALGORITHM FOR SAR IMAGES BASED ON HA...

引用

IEEE international Geoscience and Remote Sensing symposium (IGARSS)

作者： Li, Jiawei Zeng, Guobing Xu, Huaping Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

ISBN: (纸本)9798350320107

As the availability of SAR images continues to grow, efficient coregistration of massive SAR images presents a greater challenge. Traditional serial coregistration methods impose an unbearable time overhead. To reduce this overhead and make full use of computing resources, a parallel coregistration strategy based on Hadoop is proposed for SAR images. The Hadoop distributed File System (HDFS) is used to store SAR image data in chunks, and Hadoop's distributed computing strategy MapReduce is used to realize distributed parallel processing of SAR images. Two distributed parallel coregistration methods are presented with the proposed parallel strategy: one based on the maximum correlation method and the other on the DEM-assisted coregistration method. These methods are evaluated through coregistration experiments on the same dataset, and they are verified by comparing the coregistration results and processing time.

关键词： SAR Hadoop parallel Coregistration

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distributed Sparse Random Projection Trees for Constructing K-Nearest Neighbor Graphs 37

Distributed Sparse Random Projection Trees for Constructing ...

引用

37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Ranawaka, Isuru Rahmant, Md Khaledur Azad, Ariful Indiana Univ Bloomington IN 47405 USA Meta Inc Menlo Pk CA USA

ISBN: (纸本)9798350337662

A random projection tree that partitions data points by projecting them onto random vectors is widely used for approximate nearest neighbor search in high-dimensional space. We consider a particular case of random projection trees for constructing a k-nearest neighbor graph (KNNG) from highdimensional data. We develop a distributed-memory Random Projection Tree (DRPT) algorithm for constructing sparse random projection trees and then running a query on the forest to create the KNN graph. DRPT uses sparse matrix operations and a communication reduction scheme to scale KNN graph constructions to thousands of processes on a supercomputer. The accuracy of DRPT is comparable to state-of-the-art methods for approximate nearest neighbor search, while it runs two orders of magnitude faster than its peers. DRPT is available at https://***/HipGraph/DRPT.

关键词： distributed memory algorithms k nearest neighbor graph parallel algorithm

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Maximum Clique Enumeration on the GPU

Maximum Clique Enumeration on the GPU

引用

37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Geil, Afton Porumbescu, Serban D. Owens, John D. Univ Calif Davis Dept Elect & Comp Engr Davis CA 95616 USA

ISBN: (纸本)9798350311990

We present an iterative breadth-first approach to maximum clique enumeration on the GPU. The memory required to store all of the intermediate clique candidates poses a significant challenge. To mitigate this issue, we employ a variety of strategies to prune away non-maximum candidates and present a thorough examination of the performance and memory benefits of each of these options. We also explore a windowing strategy as a middle-ground between breadth-first and depth-first approaches, and investigate the resulting tradeoff between parallel efficiency and memory usage. Our results demonstrate that when we are able to manage the memory requirements, our approach achieves high throughput for large graphs indicating this approach is a good choice for GPU performance. We demonstrate an average speedup of 1.9x over previous parallel work, and obtain our best performance on graphs with low average degree.

关键词： parallel GPU maximum clique graph algorithms

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GraphTensor: Comprehensive GNN-Acceleration Framework for Efficient parallel processing of Massive Datasets 37

GraphTensor: Comprehensive GNN-Acceleration Framework for Ef...

引用

37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Jang, Junhyeok Kwon, Miryeong Gouk, Donghyun Bae, Hanyeoreum Jung, Myoungsoo Korea Adv Inst Sci & Technol KAIST Comp Architecture & Memory Syst Lab Daejeon South Korea

ISBN: (纸本)9798350337662

We present GraphTensor, a comprehensive opensource framework that supports efficient parallel neural network processing on large graphs. GraphTensor offers a set of easy-to-use programming primitives that appreciate both graph and neural network execution behaviors from the beginning (graph sampling) to the end (dense data processing). Our framework runs diverse graph neural network (GNN) models in a destination-centric, feature-wise manner, which can significantly shorten training execution times in a GPU. In addition, GraphTensor rearranges multiple GNN kernels based on their system hyperparameters in a self-governing manner, thereby reducing the processing dimensionality and the latencies further. From the end-to-end execution viewpoint, GraphTensor significantly shortens the service-level GNN latency by applying pipeline parallelism for efficient graph dataset preprocessing. Our evaluation shows that GraphTensor exhibits 1.4x better training performance than emerging GNN frameworks under the execution of large-scale, real-world graph workloads. For the end-to-end services, GraphTensor reduces training latencies of an advanced version of the GNN frameworks (optimized for multi-threaded graph sampling) by 2.4x, on average.

关键词： graph neural network large-scale graph GPU

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