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[FEA] Implement unravel for mdspan. #629

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trivialfis opened this issue Apr 26, 2022 · 1 comment
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[FEA] Implement unravel for mdspan. #629

trivialfis opened this issue Apr 26, 2022 · 1 comment
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feature request New feature or request inactive-30d

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@trivialfis
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This is an analog to numpy unravel index: https://numpy.org/doc/stable/reference/generated/numpy.unravel_index.html We often use a single index for parallel launchers like thrust::for_each, but need to index into types like matrix that have more than 1 dimension. unravel index can help us map the computation onto the parallel threads.
@trivialfis trivialfis added the feature request New feature or request label Apr 26, 2022
@trivialfis trivialfis mentioned this issue Apr 27, 2022
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This issue has been labeled inactive-30d due to no recent activity in the past 30 days. Please close this issue if no further response or action is needed. Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed. This issue will be labeled inactive-90d if there is no activity in the next 60 days.

@trivialfis trivialfis mentioned this issue Jun 24, 2022
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rapids-bot bot pushed a commit that referenced this issue Jun 29, 2022
@trivialfis
Implement unravel_index for row-major array. (#723)
12146f2 
Revised version of #631 with `thrust::tuple` replaced by `std::tuple` and custom `apply` function replaced by `std::apply`.

Related: #629

The implementation is mostly copied from XGBoost  https://github.com/dmlc/xgboost/blob/fdf533f2b9af9c068cddba50839574c6abb58dc3/include/xgboost/linalg.h#L523 .

In the tests, I have used both `__host__ __device__` and `__device__` lambdas to make sure `std::tuple` and `std::apply` are working correctly with CUDA kernel.

Authors:
  - Jiaming Yuan (https://github.com/trivialfis)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: #723
@cjnolet cjnolet closed this as completed Jul 28, 2022
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@cjnolet @trivialfis