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Naiyu Zhang
The work presented in this PhD studies and proposes cellular computation parallel models able to address different types of NP-hard optimization problems defined in the Euclidean space, and their implementation on the Graphics Processing Unit (GPU) platform. The goal is to allow both dealing with large size problems and provide substantial acceleration factors by massive […]
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Martin Rajchl, John S.H. Baxter, A. Jonathan McLeod, Jing Yuan, Wu Qiu, Terry M. Peters, James A. White, Ali R. Khan
We developed a fully-automatic multi-atlas initialized segmentation algorithm for tissue segmentation using multi-sequence MR images. The Generalized Hierarchical Max-Flow (HMF) [1] framework proposed in [2] is employed to regularize a maximum a-posteriori data term with a linear label-ordering constraint [3]. The data term is derived from two probabilistic cost functions, i) an intensity model from […]
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Peter Wittek, Sandor Daranyi
Scientific computations have been using GPU-enabled computers successfully, often relying on distributed nodes to overcome the limitations of device memory. Only a handful of text mining applications benefit from such infrastructure. Since the initial steps of text mining are typically data intensive, and the ease of deployment of algorithms is an important factor in developing […]
Peter Wittek
Somoclu is a C++ tool for training self-organizing maps on large data sets using a high-performance cluster. It builds on MPI for distributing the workload across the nodes of the cluster. It is also able to boost training by using CUDA if graphics processing units are available. A sparse kernel is included, which is useful […]
Peter Wittek, Sandor Daranyi
In this paper we introduce a MapReduce-based implementation of self-organizing maps that performs compute-bound operations on distributed GPUs. The kernels are optimized to ensure coalesced memory access and effective use of shared memory. We have performed extensive tests of our algorithms on a cluster of eight nodes with two NVidia Tesla M2050 attached to each, […]
Masato Yoshimi, Takuya Kuhara, Kaname Nishimoto, Mitsunori Miki, Tomoyuki Hiroyasu
In this study, we visualize Pareto-optimum solutions derived from multiple-objective optimization using spherical self-organizing maps (SOMs) that lay out SOM data in three dimensions. There have been a wide range of studies involving plane SOMs where Pareto-optimal solutions are mapped to a plane. However, plane SOMs have an issue that similar data differing in a […]
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Sabine McConnell, Robert Sturgeon, Gregory Henry, Andrew Mayne, Richard Hurley
We evaluate a novel implementation of a Self-Organizing Map (SOM) on a Graphics Processing Unit (GPU) cluster. Using various combinations of OpenCL, CUDA, and two different graphics cards, we demonstrate the scalability of the SOM implementation on one to eight GPUs. Results indicate that while the algorithm scales well with the number of training samples […]
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Petr Gajdos, Michal Kratky, David Bednar, Radim Baca, Radomir Gono, Jiri Walder
This paper describes a utilization of the Self Organizing Map (SOM) method for the analysis of power outage data. SOM, to be already used in many fields, is based on the Kohonen self-organizing neural network and it is known to capture underlying concepts. We apply this method for a unified database of power outages to […]
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Julia Moehrmann, Stefan Bernstein, Thomas Schlegel, Gunter Werner, Gunther Heidemann
Image recognition systems require large image data sets for the training process. The annotation of such data sets through users requires a lot of time and effort, and thereby presents the bottleneck in the development of recognition systems. In order to simplify the creation of image recognition systems it is necessary to develop interaction concepts […]
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Masahiro Takatsuka, Michael Bui
The Self-Organizing Maps (SOMs) are popular artificial neural networks that are often used for data analyses through clustering and visualisation. SOM’s mathematical model is inherently parallel. However, many implementations have not successfully exploited its parallelism because previous attempts often required cluster-like infrastructures. This article presents the parallel implementation of SOMs, particularly the batch map variant […]
Manish Shiralkar
This work describes a parallelizable optical flow field estimator based upon a modified batch version of the Self-Organizing Map (SOM). This estimator handles the ill-posedness in gradient-based motion estimation via a novel combination of regression and self-organization. The aperture problem is treated using an algebraic framework that partitions motion estimates obtained from regression into two […]
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Manish Shiralkar, Robert Schalkoff
This work describes a parallelizable optical flow field estimator based upon a modified batch version of the Self-Organizing Map (SOM). This estimator handles the ill-posedness in gradient-based motion estimation via a novel combination of regression and self-organization. The aperture problem is treated using an algebraic framework that partitions motion estimates obtained from regression into two […]
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