Speedup of Fuzzy Clustering Through Stream Processing on Graphics Processing Units

Anderson, Derek T.; Luke, Robert H.; Keller, James M.

doi:10.1109/tfuzz.2008.924203

Cited by 29 publications

(12 citation statements)

References 6 publications

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“…Anderson et al [12,1] were the first to show fuzzy logic running substantially faster by running it in parallel on a GPU.…”

Section: Gpgpu Bioinspired Algorithmsmentioning

confidence: 99%

Creating and Debugging Performance CUDA C

Langdon

2012

Parallel Architectures and Bioinspired Algorithms

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Various practical ways of testing, locating and removing bugs in parallel general-purpose computation on graphics hardware GPGPU applications are described. Some of these are generic whilst other relate directly to stochastic bioinspired techniques, such as genetic programming. We pass on software engineering lessons learnt during CUDA C programming and ways to obtain high performance from nVidia GPU and Tesla cards including examples of both successful and less successful recent applications.

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“…Anderson et al [12,1] were the first to show fuzzy logic running substantially faster by running it in parallel on a GPU.…”

Section: Gpgpu Bioinspired Algorithmsmentioning

confidence: 99%

Creating and Debugging Performance CUDA C

Langdon

2012

Parallel Architectures and Bioinspired Algorithms

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“…GPUs have been used to improve both rule construction and inference [20] and speed up fuzzy clustering [21,22]. For example Harris and Haines [21] used NVIDIA's Cg language to implement a fuzzy system.…”

Section: Fuzzy Systemsmentioning

confidence: 99%

“…They report a speed up in excess of 150 times from a single 8800 GTX. Similarly Anderson et al [22] used Cg but tested three NVIDIA GPUs (7800, Quadro FX 2500M, as well as a 8800 GTX). They report relative performance improving both with the number of processing elements in the GPU but also with the size and complexity (number of fuzzy clusters) of the data.…”

Section: Fuzzy Systemsmentioning

confidence: 99%

Graphics processing units and genetic programming: an overview

Langdon

2011

Soft Comput

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A top end graphics card (GPU) plus a suitable SIMD interpreter, can deliver a several hundred fold speed up, yet cost less than the computer holding it. We give highlights of AI and computational intelligence applications in the new field of general purpose computing on graphics hardware (GPGPU). In particular we survey genetic programming (GP) use with GPU. We give several applications from Bioinformatics and show how the fastest GP is based on an interpreter rather than compilation. Finally using GP to generate GPU CUDA kernel C++ code is sketched.

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“…Anderson & al [19] suggested a GPU solution for the Fuzzy C-Means. They have used OpenGL and Cg to achieve approximately two orders of magnitude computational speed-up for some clustering profiles using an NVIDIA8800GPU card.…”

Section: Introductionmentioning

confidence: 99%

Parallel Implementation of Bias Field Correction Fuzzy C-Means Algorithm for Image Segmentation

Aitali¹,

Cherradi²,

Abbassi³

et al. 2016

ijacsa

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Abstract-Image segmentation in the medical field is one of the most important phases to diseases diagnosis. The bias field estimation algorithm is the most interesting techniques to correct the in-homogeneity intensity artifact on the image. However, the use of such technique requires a powerful processing and quite expensive for big size as medical images. Hence the idea of parallelism becomes increasingly required. Several researchers have followed this path mainly in the bioinformatics field where they have suggested different algorithms implementations. In this paper, a novel Single Instruction Multiple Data (SIMD) architecture for bias field estimation and image segmentation algorithm is proposed. In order to accelerate compute-intensive portions of the sequential implementation, we have implemented this algorithm on three different graphics processing units (GPU) cards named GT740m, GTX760 and GTX580 respectively, using Compute Unified Device Architecture (CUDA) software programming tool. Numerical obtained results for the computation speed up, allowed us to conclude on the suitable GPU architecture for this kind of applications and closest ones.

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Speedup of Fuzzy Clustering Through Stream Processing on Graphics Processing Units

Cited by 29 publications

References 6 publications

Creating and Debugging Performance CUDA C

Creating and Debugging Performance CUDA C

Graphics processing units and genetic programming: an overview

Parallel Implementation of Bias Field Correction Fuzzy C-Means Algorithm for Image Segmentation

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