GPU Acceleration - V4: Difference between revisions
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This page outlines the proposed GPU acceleration framework in ITK v4. | This page outlines the proposed GPU acceleration framework in ITK v4. The GPU has become a cost-effective parallel computing platform for computationally expensive problems. Although many ITK image filters can benefit from the GPU, there has been no GPU support in ITK as of today. We propose to add a new data structure, framework, and some basic image operations that support the GPU in order to allow ITK developers can easily implement their filters running on both the CPU and GPU. | ||
== Goals == | == Goals == | ||
Revision as of 15:37, 20 October 2010
This page outlines the proposed GPU acceleration framework in ITK v4. The GPU has become a cost-effective parallel computing platform for computationally expensive problems. Although many ITK image filters can benefit from the GPU, there has been no GPU support in ITK as of today. We propose to add a new data structure, framework, and some basic image operations that support the GPU in order to allow ITK developers can easily implement their filters running on both the CPU and GPU.
Goals
- Add the support for the GPU processing in ITK
- GPU image class
- Extension of filter architecture to support the GPU
- Basic GPU image operators
Authors
GPU supports in ITK v4 has been proposed by Harvard University and University of Utah.
- Won-Ki Jeong (wkjeong -at- seas.harvard.edu)
- Hanspeter Pfister (pfister -at- seas.harvard.edu)
- Ross Whitaker (whitaker -at- cs.utah.edu)
Plans
- GPU image class
- Filter architecture supporting the GPU
- Basic GPU image operators
