An Introduction to General-Purpose Computing on Programmable Graphics Hardware

2007 ◽  
pp. 123-161 ◽  
Author(s):  
Tor Dokken ◽  
Trond Runar Hagen ◽  
Jon Mikkelsen Hjelmervik
Keyword(s):  
General Purpose ◽  
Graphics Hardware

Volumenvisualisierung auf handelsüblicher Grafik-Hardware (Volume Rendering Techniques for General Purpose Graphics Hardware)

2005 ◽  
Vol 47 (1) ◽  
Author(s):  
Christof Rezk Salama
Keyword(s):  
Volume Rendering ◽  
General Purpose ◽  
Graphics Hardware ◽  
Rendering Techniques

AbstractTechniken der Volumenvisualisierung werden zur räumlichen Darstellung dreidimensionaler Skalarfelder benötigt, wie sie beispielsweise in der Medizin in Form von tomografischen Daten entstehen. Diese Arbeit beschäftigt sich mit Ansätzen, hochqualitative Bilder solcher Volumendaten in Echtzeit mithilfe handelsüblicher Grafikkarten zu erzeugen.

AN EVALUATION OF MULTIPLE FEED-FORWARD NETWORKS ON GPUs

2011 ◽  
Vol 21 (01) ◽  
pp. 31-47 ◽  
Author(s):  
NOEL LOPES ◽  
BERNARDETE RIBEIRO
Keyword(s):  
Graphics Processing Unit ◽  
Parallel Implementation ◽  
Low Cost ◽  
Back Propagation ◽  
General Purpose ◽  
Training System ◽  
Graphics Hardware ◽  
Processing Unit ◽  
Data Parallel ◽  
Graphics Processing

The Graphics Processing Unit (GPU) originally designed for rendering graphics and which is difficult to program for other tasks, has since evolved into a device suitable for general-purpose computations. As a result graphics hardware has become progressively more attractive yielding unprecedented performance at a relatively low cost. Thus, it is the ideal candidate to accelerate a wide variety of data parallel tasks in many fields such as in Machine Learning (ML). As problems become more and more demanding, parallel implementations of learning algorithms are crucial for a useful application. In particular, the implementation of Neural Networks (NNs) in GPUs can significantly reduce the long training times during the learning process. In this paper we present a GPU parallel implementation of the Back-Propagation (BP) and Multiple Back-Propagation (MBP) algorithms, and describe the GPU kernels needed for this task. The results obtained on well-known benchmarks show faster training times and improved performances as compared to the implementation in traditional hardware, due to maximized floating-point throughput and memory bandwidth. Moreover, a preliminary GPU based Autonomous Training System (ATS) is developed which aims at automatically finding high-quality NNs-based solutions for a given problem.

General Purpose Computation on Graphics Hardware

2006 ◽  
Author(s):  
A. Lefohn ◽  
I. Buck ◽  
P. McCormick ◽  
J. Owens ◽  
T. Purcell ◽  
...  
Keyword(s):  
General Purpose ◽  
Graphics Hardware

scholarly journals Vis3D+ A tightly integrated GPU-accelerated computation and rendering framework for interactive 3D image visualization

2021 ◽  
Author(s):  
Irfa Nisar
Keyword(s):  
Image Processing ◽  
Graphics Processing Units ◽  
General Purpose ◽  
Graphics Hardware ◽  
3D Image ◽  
Image Processing Algorithm ◽  
Image Visualization ◽  
Computing Platform ◽  
Software Modules ◽  
Interactive 3D

This thesis presents extensions to an interactive 3D image visualization framework. The existing software framework provides functionality for interactively visualizing 3D medical data. The extensions consist of software modules that execute directly on the graphics hardware, utilizing the massively parallel, general-purpose computing platform provided by modern graphics processing units (GPUs). These GPUbased software modules are designed to support the execution of volume image processing algorithms, implemented using recently available GPU programs known as “compute shaders”, as well as to support interactive editing of the algorithms’ output. The new modules are seamlessly integrated as new stages in a GPU-based rendering pipeline provided by the existing framework. In this thesis, an example volume image processing algorithm known as level set segmentation is implemented and demonstrated. In addition, a new editing module is demonstrated that enables user modification of this algorithm’s output by extending a pre-existing volume “painting” interface.

scholarly journals A Survey of General-Purpose Computation on Graphics Hardware

2007 ◽  
Vol 26 (1) ◽  
pp. 80-113 ◽  
Author(s):  
John D. Owens ◽  
David Luebke ◽  
Naga Govindaraju ◽  
Mark Harris ◽  
Jens Krüger ◽  
...  
Keyword(s):  
General Purpose ◽  
Graphics Hardware

scholarly journals Vis3D+ A tightly integrated GPU-accelerated computation and rendering framework for interactive 3D image visualization

2021 ◽  
Author(s):  
Irfa Nisar
Keyword(s):  
Image Processing ◽  
Graphics Processing Units ◽  
General Purpose ◽  
Graphics Hardware ◽  
3D Image ◽  
Image Processing Algorithm ◽  
Image Visualization ◽  
Computing Platform ◽  
Software Modules ◽  
Interactive 3D

This thesis presents extensions to an interactive 3D image visualization framework. The existing software framework provides functionality for interactively visualizing 3D medical data. The extensions consist of software modules that execute directly on the graphics hardware, utilizing the massively parallel, general-purpose computing platform provided by modern graphics processing units (GPUs). These GPUbased software modules are designed to support the execution of volume image processing algorithms, implemented using recently available GPU programs known as “compute shaders”, as well as to support interactive editing of the algorithms’ output. The new modules are seamlessly integrated as new stages in a GPU-based rendering pipeline provided by the existing framework. In this thesis, an example volume image processing algorithm known as level set segmentation is implemented and demonstrated. In addition, a new editing module is demonstrated that enables user modification of this algorithm’s output by extending a pre-existing volume “painting” interface.

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