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@JoOkuma I am wondering if this is the proper wayof calling the back ends, as you kindly explained in this issue. I would like to measure the performance of the Numpy vs. Cupy backends. in other words a process based exclusively on CPU vs the boost in performance using the CuPy GPU backend. I am posting the code I used to generate this test. I have a 32x speed increase using the Cupy backend (uhuu!!!). I want to make sure I am not missing something. The results (see below) indicate that I effectively was using Numpy vs Cupy backends "effectively". Nevertheless I observed a spike of function in my GPU using the Numpy backend. I was expecting that the Numpy backend was exclusively CPU based. Did I understood this wrong?
Results:
Yang <<CupyBackend>> Deskewing completed. Execution Time: 0.5733585357666016 seconds
├ Number of free blocks before and after release: 4->0, used: 0.055 GBs -> 0.055 GBs, total:0.055 GBs
Yang <<NumpyBackend>> Deskewing completed. Execution Time: 19.52552628517151 seconds
@JoOkuma I am wondering if this is the proper wayof calling the back ends, as you kindly explained in this issue. I would like to measure the performance of the Numpy vs. Cupy backends. in other words a process based exclusively on CPU vs the boost in performance using the CuPy GPU backend. I am posting the code I used to generate this test. I have a 32x speed increase using the Cupy backend (uhuu!!!). I want to make sure I am not missing something. The results (see below) indicate that I effectively was using Numpy vs Cupy backends "effectively". Nevertheless I observed a spike of function in my GPU using the Numpy backend. I was expecting that the Numpy backend was exclusively CPU based. Did I understood this wrong?
Results:
Code
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