ITK/Release 4/GPU Acceleration/Tcon-2010-11-22: Difference between revisions
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< ITK | Release 4 | GPU Acceleration
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** Joe answers | ** Joe answers | ||
** OpenCL is better for asynchronous multi-GPU programming. | ** OpenCL is better for asynchronous multi-GPU programming. | ||
** Reasons for | ** Reasons for using CUDA over OpenCL | ||
*** Tedious API | *** Tedious API in OpenCL | ||
*** large collection of CUDA existing libraries | |||
*** large collection of existing libraries | |||
Revision as of 18:29, 22 November 2010
Attendees
- Won Ki - Harvard University
- Joe Stem - NVIDIA
- Kimberly Powell - NVIDIA
- Dennis Sessanna - NVIDIA
- Luis Ibanez - Kitware Inc.
Topics
Overview
- Quick summary of ITKv4 effort (Luis Ibanez)
- Summary of ITK-GPU approach (Won Ki)
Questions
- Level of abstraction ?
- Joe suggests to look at OpenCV
- Expose the interactions with the GPU
- Most GPU programmers do things synchronously (so they unfortunately do too many data transfers, and don't get full benefit from the GPU).
- Joe suggests to look at OpenCV
- Joe asked for typical Use Cases
- We listed:
- Radiology : 100Mb per image (512x512x200)
- Microscopy : 10Gb
- Video : 10Mb images, 30~100 frames per second.
- We listed:
- CUDA vs OpenCL ?
- Joe answers
- OpenCL is better for asynchronous multi-GPU programming.
- Reasons for using CUDA over OpenCL
- Tedious API in OpenCL
- large collection of CUDA existing libraries
