ITK/Release 4/GPU Acceleration/Tcon-2010-11-22: Difference between revisions
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*** Performance optimization may be harder in OpenCL | *** Performance optimization may be harder in OpenCL | ||
** Luis asked about vendor's commitment to OpenCL (for next 5 ~ 10 years) | ** Luis asked about vendor's commitment to OpenCL (for next 5 ~ 10 years) | ||
*** Joe answers | |||
**** NVIDIA supports the OpenCL standard (so, what is in OpenCL will be supported in NVidia cards) | |||
Revision as of 18:33, 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
- Performance optimization may be harder in OpenCL
- Luis asked about vendor's commitment to OpenCL (for next 5 ~ 10 years)
- Joe answers
- NVIDIA supports the OpenCL standard (so, what is in OpenCL will be supported in NVidia cards)
- Joe answers
