Community detection is a vital task in many fields,such as social networks and financial analysis,to name a *** Louvain method,the main workhorse of community detection,is a popular heuristic *** apply it to large-sca...
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Community detection is a vital task in many fields,such as social networks and financial analysis,to name a *** Louvain method,the main workhorse of community detection,is a popular heuristic *** apply it to large-scale graph networks,researchers have proposed several parallel Louvain methods(PLMs),which suffer from two challenges:the latency in the information synchronization,and the community *** tackle these two challenges,we propose an isolate sets based parallel Louvain method(IPLM)and a fusion IPLM with the hashtables based Louvain method(FIPLM),which are based on a novel graph partition *** graph partition algorithm divides the graph network into subgraphs called isolate sets,in which the vertices are relatively decoupled from *** first describe the concepts and properties of the isolate *** we propose an algorithm to divide the graph network into isolate sets,which enjoys the same computation complexity as the breadth-first ***,we propose IPLM,which can efficiently calculate and update vertices information in parallel without latency or community ***,we achieve further acceleration by FIPLM,which maintains a high quality of community detection with a faster speedup than *** two methods are for shared-memory architecture,and we implement our methods on an 8-core PC;the experiments show that IPLM achieves a maximum speedup of 4.62x and outputs higher modularity(maximum 4.76%)than the serial Louvain method on 14 of 18 ***,FIPLM achieves a maximum speedup of 7.26x.
In computational fluid dynamics(CFD),mesh-smoothing methods are widely used to refine the mesh quality for achieving high-precision numerical ***,optimization-based smoothing is used for high-quality mesh smoothing,bu...
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In computational fluid dynamics(CFD),mesh-smoothing methods are widely used to refine the mesh quality for achieving high-precision numerical ***,optimization-based smoothing is used for high-quality mesh smoothing,but it incurs significant computational *** works have improved its smoothing efficiency by adopting supervised learning to learn smoothing methods from high-quality ***,they pose difficulties in smoothing the mesh nodes with varying degrees and require data augmentation to address the node input sequence ***,the required labeled high-quality meshes further limit the applicability of the proposed *** this paper,we present graph-based smoothing mesh net(GMSNet),a lightweight neural network model for intelligent mesh *** adopts graph neural networks(GNNs)to extract features of the node’s neighbors and outputs the optimal node *** smoothing,we also introduce a fault-tolerance mechanism to prevent GMSNet from generating negative volume *** a lightweight model,GMSNet can effectively smooth mesh nodes with varying degrees and remain unaffected by the order of input data.A novel loss function,MetricLoss,is developed to eliminate the need for high-quality meshes,which provides stable and rapid convergence during *** compare GMSNet with commonly used mesh-smoothing methods on two-dimensional(2D)triangle *** results show that GMSNet achieves outstanding mesh-smoothing performances with 5%of the model parameters compared to the previous model,but offers a speedup of 13.56 times over the optimization-based smoothing.
All-reduce is a widely used communication technique for distributed and parallel applications typically implemented using either a tree-based or ring-based scheme. Each of these approaches has its own limitations: tre...
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All-reduce is a widely used communication technique for distributed and parallel applications typically implemented using either a tree-based or ring-based scheme. Each of these approaches has its own limitations: tree-based schemes struggle with efficiently exchanging large messages, while ring-based solutions assume constant communication throughput,an unrealistic expectation in modern network communication infrastructures. We present FMCC-RT, an all-reduce approach that combines the advantages of tree-and ring-based implementations while mitigating their drawbacks. FMCC-RT dynamically switches between tree and ring-based implementations depending on the size of the message being processed. It utilizes an analytical model to assess the impact of message sizes on the achieved throughput, enabling the derivation of optimal work partitioning parameters. Furthermore, FMCC-RT is designed with an Open MPI-compatible API, requiring no modification to user code. We evaluated FMCC-RT through micro-benchmarks and real-world application tests. Experimental results show that FMCC-RT outperforms state-of-the-art tree-and ring-based methods, achieving speedups of up to 5.6×.
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning *** this approach allows models to specialize in specific tasks w...
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Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning *** this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader ***-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational *** these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA *** these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains *** study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM *** investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do *** insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized ***,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller *** study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM
Innovations in powerful high-performance computing (HPC) architecture are enabling high-fidelity whole-core neutron transport simulations at reasonable time. Especially, the currently fashionable heterogeneous archite...
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Recurrent neural networks (RNNs) have become common models in the field of artificial intelligence to process temporal sequence task, such as speech recognition, text analysis, natural language processing, etc. To spe...
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Serialization and deserialization play a dominant role in the state transfer time of serverless workflows, leading to substantial performance penalties during workflow execution. We identify the key reason as a lack o...
Motion and appearance cues play a crucial role in Multi-object Tracking (MOT) algorithms for associating objects across consecutive frames. While most MOT methods prioritize accurate motion modeling and distincti...
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Recent advances in single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for reconstruction gene regulation networks (GRNs). At present, many different models have been proposed to inf...
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4K Video Super-Resolution (VSR) presents a challenging task in video processing, as most existing VSR models have high computational complexity, limiting their application to high-resolution videos, particularly for 4...
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