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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/02/12 23:44:15 UTC
[GitHub] sandeep-krishnamurthy opened a new pull request #14139: Performance
improvement in Normalize GPU Kernel
sandeep-krishnamurthy opened a new pull request #14139: Performance improvement in Normalize GPU Kernel
URL: https://github.com/apache/incubator-mxnet/pull/14139
## Description ##
Similar to perf improvements of ToTensor GPU kernel in PR - https://github.com/apache/incubator-mxnet/pull/14099
In this PR, I wrote a separate CUDA kernel for GPU and moved out of Kernel launch/map.
Benchmarks below.
**Benchmarks**
Ran 500 Normalize operation on (3, 512, 512) sample input.
**GPU**
Before: ('Average time per Normalize 3,512,512 - ', 38.19581985473633)
After: ('Average time per Normalize 3,512,512 - ', 0.5398507118225098)
**CPU**
Before: ('Average time per Normalize 3,512,512 - ', 1.8209707736968994)
After: ('Average time per Normalize 3,512,512 - ', 1.2644755840301514)
('Total time for CPU ToTensor - ', 1264.4755840301514)
('Average time per Normalize 3,512,512 - ', 1.2644755840301514)
## Checklist ##
### Essentials ###
Please feel free to remove inapplicable items for your PR.
- [X] Changes are complete (i.e. I finished coding on this PR)
- [X] All changes have test coverage:
- [X] Code is well-documented:
- For user-facing API changes, API doc string has been updated.
- For new C++ functions in header files, their functionalities and arguments are documented.
- For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
- [X] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
### Changes ###
- Remove Kernel Launch/Map for Normalize operator
- Make independent kernel for CPU Normalize
- Add 2 separate CUDA kernel for Normalize Forward operator (3D and 4D) inputs.
- Add 2 separate CUDA kernel for Normalize Backward operator (3D and 4D) inputs.
@stu1130 @zhreshold
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