GPU-FS-kNN: A Software Tool for Fast and Scalable kNN Computation Using GPUs
نویسندگان
چکیده
منابع مشابه
GPU-FS-kNN: A Software Tool for Fast and Scalable kNN Computation Using GPUs
BACKGROUND The analysis of biological networks has become a major challenge due to the recent development of high-throughput techniques that are rapidly producing very large data sets. The exploding volumes of biological data are craving for extreme computational power and special computing facilities (i.e. super-computers). An inexpensive solution, such as General Purpose computation based on ...
متن کاملGPU-SME-kNN: Scalable and memory efficient kNN and lazy learning using GPUs
The k nearest neighbor (kNN) rule is one of the most used techniques in data mining and pattern recognition due to its simplicity and low identification error. However, the computational effort it requires is directly related to the dataset sizes, hence delivering a poor performance on large datasets. ::: The :::: use :: of :::::::: graphics processing units (GPU) ::: has :::::::: improved ::::...
متن کاملkNN-Borůvka-GPU: A Fast and Scalable MST Construction from kNN Graphs on GPU
Computation of the minimum spanning tree (MST) is a common task in numerous fields of research, such as pattern recognition, computer vision, network design (telephone, electrical, hydraulic, cable TV, computer, road networks etc.), VLSI layout, to name a few. However, for a large-scale dataset when the graphs are complete, classical MST computation algorithms become unsuitable on general purpo...
متن کاملPARALLEL kNN ON GPU ARCHITECTURE USING OpenCL
In data mining applications, one of the useful algorithms for classification is the kNN algorithm. The kNN search has a wide usage in many research and industrial domains like 3-dimensional object rendering, content-based image retrieval, statistics, biology (gene classification), etc. In spite of some improvements in the last decades, the computation time required by the kNN search remains the...
متن کاملOn Ranking Nodes using kNN Graphs, Shortest-paths and GPUs
In this paper, we present graphics processing unit (GPU) based implementations of three popular shortest-path centrality metricscloseness, eccentricity and betweenness. The basic method is designed to compute the centrality on gene-expression networks, where the network is pre-constructed in the form of kNN graphs from DNA microarray data sets. The relationship among the genes in the kNN graph ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLoS ONE
سال: 2012
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0044000