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ENH: kernels for `random.vonmisses`; part 2
Description
Enable computations on devices [CPU/GPU].
Tests
- DPNP own:
tests/test_random.py::TestDistributionsVonmises::test_moments[large_kappa] PASSED
tests/test_random.py::TestDistributionsVonmises::test_moments[small_kappa] PASSED
tests/test_random.py::TestDistributionsVonmises::test_invalid_args PASSED
tests/test_random.py::TestDistributionsVonmises::test_seed[large_kappa] FAILED
tests/test_random.py::TestDistributionsVonmises::test_seed[small_kappa] FAILED
- + numpy external
TODO
tests/test_random.py::TestDistributionsVonmises::test_seed failed on both devices. Bug.
@samir-nasibli Is this PR ready to review or still in development stage?
@samir-nasibli Is this PR ready to review or still in development stage?
I will update this PR or move some part of this changes to another PR with closing this.
@LukichevaPolina The use of extra memory with the amount of data is not a good practice in optimization. We must avoid this cases. We have to remove the possibilities for a potential race condition in the algorithm.
The use of extra memory with the amount of data is not a good practice in optimization. We must avoid this cases. - ideas? We have to remove the possibilities for a potential race condition in the algorithm. - done
The use of extra memory with the amount of data is not a good practice in optimization. We must avoid this cases. - ideas? We have to remove the possibilities for a potential race condition in the algorithm. - done
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Any optimization with the use of additional memory can actually degrade (depending on the input data) and underestimate all the benefits from parallelism. Allocation/Deallocation/Working with additional memory is expensive.
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We have to remove the possibilities for a potential race condition in the algorithm. - done
using extra mem is brute force approach.
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- ideas?
We need to investigate it.
- I also recommend to use perf tests and profiler tools during optimization. Comparative analysis is important in such work.
Stale PR?