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-n does not seem to be honored #17
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Yes, the semantics of -n has changed. -n now stands for "split the problem in N subproblems to be run in sequence", with the reasoning being that we use less memory during Unifrac compute. This is especially useful for GPU compute. The number of threads is fully controlled by the OpenMP threading library, i.e. OMP_NUM_THREADS. |
I think we should correctly support the threads parameters from the python interface. Would it suffice to use Python to set OMP_NUM_THREADS? W/ documentation, I think we should update the ssu usage string to reflect what -n does, I think a new parameter name probably makes sense, and to also note specifically in the usage string the use of OMP_NUM_THREADS |
For context: |
Ya, good point
… On Nov 29, 2022, at 9:33 AM, Igor Sfiligoi ***@***.***> wrote:
For context:
NumPy typically manages thread parallelism with OMP_NUM_THREAD:
https://numpy.org/devdocs/reference/global_state.html <https://numpy.org/devdocs/reference/global_state.html>
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Let's deprecate -n and document it accordingly. |
Thanks!
… On Nov 29, 2022, at 10:24 AM, Igor Sfiligoi ***@***.***> wrote:
Let's deprecate -n and document it accordingly.
I will add a separate flag for the problem splitting.
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Released in 1.2. |
I'm running
ssu ... -n 8
however, it appears thatssu
is using 16 threadsThe text was updated successfully, but these errors were encountered: