ClusteringThe k -means algorithm is widely used for clustering, compressing, and summarizing vector data. We present a fast and memory-efficient GPU-based algorithm for exact k -means, Asynchronous Selective Ba
Despite the promising progress that has been made, large-scale clustering tasks still face various challenges: (i) high time and space complexity in K-near
Large scale K-means clustering using GPUs The k-means algorithm is widely used for clustering, compressing, and summarizing vector data. We present a fast and memory-efficient GPU-based algorithm f... M Li,E Frank,B Pfahringer - 《Data Mining & Knowledge Discovery》 被引量: 0发表: 2023年...
3 Real-time extraction of large-scale neuronal activities enables ensemble activity-triggered feedback. a, Experimental design of ensemble activity-triggered closed-loop experiments. Brain-wide neurons are segmented from the reference stack. K-means clustering of their spontaneous activities generated ...
Angular (cosine) distance metric effectively results in Spherical K-Means behavior. The samplesmustbe normalized to L2 norm equal to 1 before clustering, it is not done automatically. The actual formula is: If you get OOM with the default parameters, setyinyang_tto 0 which forces Lloyd.verbosi...
As the utilization of Deep Learning and AI continues to expand, the models have become increasingly complex and larger in size
Large-Scale Geospatial Pro- cessing on Multi-Core and Many-Core Processors: Evalua- tions on CPUs, GPUs and MICs. CoRR abs/1403.0802J. Zhang and S. You, "Large-Scale Geospatial Processing on Multi- Core and Many-Core Processors: Evaluations on CPUs, GPUs and MICs," CoRR, vol. abs/...
Specifically, we attempt to cluster similar or dependent code files together using methods like Calling Graph, K-Means clustering, file path similarity, and TF-IDF distance, to help the model better understand the relationships between code files. However, the ordering of code files also ...
FPGAs have been used to implement and accelerate important data- center applications such as Memcached,14,15 compression and decom- pression,16,17 k-means clustering,18,19 and Web search. Researchers have used FPGAs to accelerate search,20,21 but they focused primarily on the selection stage ...
This paper focuses on using GPUs as co-processors for sorting. We propose a new mapping of bitonic sorting network on GPUs. We started from its traditional algorithm, and we extend it to adapt to our target architecture. Bitonic sorting network is one of the fastest sorting networks to run ...