Add to ORKGGraphics processing units (GPUs) are powerful com-putational devices tailored towards the needs of the 3-D gaming industry for high-performance, real-time graphics engines. Nvidia Corporation provides a programming lan-guage called CUDA for general-purpose GPU program-ming. Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces; if there are n data points, it can be computed in O(n2) to O(n2 log n) sequential time, depending on the distance metrics employed. The present work explores paral-lel computation of hierarchical clustering with CUDA/GPU, and obtains an overall speed-up of up to 48 times over sequential computation with an Intel Pentium CPU. 1
Graphics Processing Units (GPUs) are a fast evolving architecture. Over the last decade their progra...
Density-based clustering algorithms are widely used unsupervised data mining techniques to find the ...
The ability to process, visualise, and work with large volumes of data in a way that is fast, meanin...
<p>Clustering can be considered the most important unsupervised learning<br>technique. Clustering is...
10.1007/978-3-642-13672-6_4Lecture Notes in Computer Science (including subseries Lecture Notes in A...
Hierarchical clustering is a common tool for simplification, exploration, and analysis of datasets i...
Hierarchical clustering algorithms are common tools for simplifying, exploring and analyzing dataset...
During the last few years, GPUs have evolved from simple devices for the display signal preparation ...
The computational demands of multivariate clustering grow rapidly, and therefore processing large da...
ITS cluster finding algorithm is one of the data reduction algorithms at ALICE. It needs to be proce...
The purpose of this paper is to describe the key points of the implementation of clustering algorith...
Abstract. During the last few years, Graphics Processing Units (GPU) have evolved from simple device...
Clustering approaches are widely used methodologies to analyse large data sets. The K-means algorith...
Graphics Processing Units (GPUs) are used together with the CPU to accelerate a wide range of genera...
Abstract—Cluster analysis plays a critical role in a wide variety of applications; but it is now fac...
Graphics Processing Units (GPUs) are a fast evolving architecture. Over the last decade their progra...
Density-based clustering algorithms are widely used unsupervised data mining techniques to find the ...
The ability to process, visualise, and work with large volumes of data in a way that is fast, meanin...
<p>Clustering can be considered the most important unsupervised learning<br>technique. Clustering is...
10.1007/978-3-642-13672-6_4Lecture Notes in Computer Science (including subseries Lecture Notes in A...
Hierarchical clustering is a common tool for simplification, exploration, and analysis of datasets i...
Hierarchical clustering algorithms are common tools for simplifying, exploring and analyzing dataset...
During the last few years, GPUs have evolved from simple devices for the display signal preparation ...
The computational demands of multivariate clustering grow rapidly, and therefore processing large da...
ITS cluster finding algorithm is one of the data reduction algorithms at ALICE. It needs to be proce...
The purpose of this paper is to describe the key points of the implementation of clustering algorith...
Abstract. During the last few years, Graphics Processing Units (GPU) have evolved from simple device...
Clustering approaches are widely used methodologies to analyse large data sets. The K-means algorith...
Graphics Processing Units (GPUs) are used together with the CPU to accelerate a wide range of genera...
Abstract—Cluster analysis plays a critical role in a wide variety of applications; but it is now fac...
Graphics Processing Units (GPUs) are a fast evolving architecture. Over the last decade their progra...
Density-based clustering algorithms are widely used unsupervised data mining techniques to find the ...
The ability to process, visualise, and work with large volumes of data in a way that is fast, meanin...