Efficient Parallel Algorithm for Nonlinear Dimensionality Reduction on GPU

Yeh, Tsung Tai; Chen, Tseng-Yi; Chen, Yen-Chiu; Shih, Wei-Kuan

doi:10.1109/grc.2010.145

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…Their approach subdivides the data into cells in order to gain spatial locality and efficient parallelisation. Yeh et al [16] developed an efficient GPU-based k-NN search using kd-tree, where they performed fast parallel radix sort for calculating the median values in kd-tree construction. Garcia et al [17] proposed a newer GPU-based k-NN approach, by using the cuBLAS (CUDA Basic Linear Algebra Subroutines) library in order to efficiently calculate a parallel distance matrix for faster brute-force k-NN parallelisation.…”

Section: Related Workmentioning

confidence: 99%

Fast Approximate k-Nearest Neighbours Search Using GPGPU

Lukač

Žalik

2014

GPU Computing and Applications

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The k-nearest neighbours (k-NN) search is one of the most critical non-parametric methods used in data retrieval and similarity tasks. Over recent years, fast k-NN processing for large amount of high-dimensional data is increasingly demanded. Locality-sensitive hashing is a viable solution for computing fast approximate nearest neighbours (ANN) with reasonable accuracy. This chapter presents a novel parallelisation of the locality-sensitive hashing method using GPGPU, where the multi-probe variant is considered. The method was implemented using CUDA platform for constructing a k-ANN graph. It was compared to the state-of-the-art CPU-based k-ANN and two GPU-based k-NN methods on large and multidimensional data set. The experimental results showed that the proposed method has a speed-up of 30Â or higher, in comparison to the CPU-based approximate method, whilst retaining a high recall rate.

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Section: Related Workmentioning

confidence: 99%

Fast Approximate k-Nearest Neighbours Search Using GPGPU

Lukač

Žalik

2014

GPU Computing and Applications

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“…The importance of efficiently using memory in CUDA cannot be overstated. There are three orders of magnitude un similar speed between the fastest on-chip register memory and mapped host memory that should be traverse the PCIe bus, CUDA developers must have understanding about the most efficient way to memory used [13,17].…”

Section: Efficient Cuda Memory Using Reduction Kernelmentioning

confidence: 99%

GPU Application in Cuda Memory

Khoirudin¹,

Shun-Liang²

2015

ACIJ

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Nowadays modern computer GPU (Graphic Processing Unit) became widely used to improve the performance of a computer, which is basically for the GPU graphics calculations, are now used not only for the purposes of calculating the graphics but also for other application. In addition, Graphics Processing Unit (GPU) has high computation and low price. This device can be treat as an array of SIMD processor using CUDA software. This paper talks about GPU application, CUDA memory and efficient CUDA memory using Reduction kernel. High-performance GPU application requires reuse of data inside the streaming multiprocessor (SM). The reason is that onboard global memory is simply not fast enough to meet the needs of all the streaming multiprocessor on the GPU. In addition, CUDA exposes the memory space within the SM and provides configurable caches to give the developer the greatest opportunity of data reuse.

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“…This approach has many advantages such as easy to implement, suitable for most of cases, fast enough and can be parallelized and further accelerated [2]. But for some other type of dataset, the k-nn approach may face difficulties.…”

Section: Introductionmentioning

confidence: 99%

About Eigenvalues from Embedding Data Complex in Low Dimension

Liou

2012

Lecture Notes in Computer Science

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Abstract. LLE(Local linear embedding) and Isomap are widely used approaches for dimension reduction on data complex. The embedding results from the two methods are eigenvectors from solving specific matrices. The corresponding eigenvalues for the selected eigenvectors have important meaning for the embedding results. In this paper, the k-nn method and -distance approach are used for neighborhood function with parameters. Then, different datasets and parameters will be applied to obtain the embedding results and eigenvalues. The main change of eigenvalues and the corresponding embedding results will be shown in this paper.

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Efficient Parallel Algorithm for Nonlinear Dimensionality Reduction on GPU

Cited by 10 publications

References 7 publications

Fast Approximate k-Nearest Neighbours Search Using GPGPU

Fast Approximate k-Nearest Neighbours Search Using GPGPU

GPU Application in Cuda Memory

About Eigenvalues from Embedding Data Complex in Low Dimension

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