DSP holds significant potential for important applications in Deep Neural Networks. However, there is currently a lack of research focused on shared-memory CPU-DSP heterogeneous chips. This paper proposes CD-Sched, an...
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Photometry is an important optical characteristic of space targets. Due to the huge computational volume of the photometry of the full-angle of a space target, the computation time is long and is difficult to meet the...
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Appling mathematical modeling methods for analyses synchronization processes of distributed data processing systems makes it possible to significantly optimize these processes and guarantee the required level of consi...
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This research presents a novel parallel model for Particle Swarm Optimization (PSO), named Multi-agent Agent-based PSO (MAPSO). The model synergizes multi-agent system principles with parallelcomputing techniques to ...
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The rapid growth of cloud computing has brought new challenges in parallel Batch Machine Scheduling (PBMS), particularly when incorporating malleability and rejection constraints. This has led to the parallel Batch Ma...
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Cloud computing is widely being used by researchers’ academia and industry for its abundant opportunities. Different technologies such as Internet of Things, Edge computing, and Fog computing are gradually integratin...
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To detect the DDoS (distributed Denial of Service) attack that initiates a flooding attack over a targeted server or service meanwhile causing network traffic and disrupting legitimate users from accessing the network...
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Virtual machines (VMs) in cloud computing can be transferred from one physical host to another via VM migration. The placement and migration of virtual machines are a multi-objective optimization problem. An effective...
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Edge computing is a rapidly developing research area known for its ability to reduce latency and improve energy efficiency, and it also has a potential for green computing. Many geographically distributed edge servers...
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Community detection is the problem of finding naturally forming clusters in networks. It is an important problem in mining and analyzing social and other complex networks. Community detection can be used to analyze co...
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ISBN:
(纸本)9783031785405;9783031785412
Community detection is the problem of finding naturally forming clusters in networks. It is an important problem in mining and analyzing social and other complex networks. Community detection can be used to analyze complex systems in the real world and has applications in many areas, including network science, data mining, and computational biology. Label propagation is a community detection method that is simpler and faster than other methods such as Louvain, InfoMap, and spectral-based approaches. Some real-world networks can be very large and have billions of nodes and edges. Sequential algorithms might not be suitable for dealing with such large networks. This paper presents distributed-memory and hybrid parallel community detection algorithms based on the label propagation method. We incorporated novel optimizations and communication schemes, leading to very efficient and scalable algorithms. We also discuss various load-balancing schemes and present their comparative performances. These algorithms have been implemented and evaluated using large high-performance computing systems. Our hybrid algorithm is scalable to thousands of processors and has the capability to process massive networks. This algorithm was able to detect communities in the Metaclust50 network, a massive network with 282 million nodes and 42 billion edges, in 654 s using 4096 processors.
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