Rewrite ApplySymmetryToTensor3 with NumPy/BLAS (11x speedup) - #125
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Rewrite ApplySymmetryToTensor3 with NumPy/BLAS (11x speedup)#125psavery wants to merge 1 commit into
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Profiling the free-energy Hessian of a 128-atom PdH supercell in python-sscha showed 82% of the run inside this method: the Fortran symph routines apply the symmetries element by element in serial. The three steps are redone with numpy array operations: the permutation symmetry as an average over the 6 index orderings, the translation average computed once per group of equivalent atoms, and each symmetry at gamma applied as one matrix product over the cartesian components. On the PdH case: 181 s -> 16 s, matching the old result to 1e-16. Also checked on two lower-symmetry cells (120 and 216 atoms). Peak memory grows because of numpy temporaries: on the 216-atom cell about 11 GB against 6.5 GB, with the time going 211 s -> 34 s. The matrix products use numpy's threaded BLAS but do not depend on it: on the 120-atom cell the Fortran path takes 20.4 s and the numpy one 6.5 s on a single thread (3.8 s with threads on a 24-core machine). On shared nodes without core pinning, cap the threads with OPENBLAS_NUM_THREADS. The Fortran routines (permute_v3, trans_v3, sym_v3) no longer have callers here and can be removed in a follow-up if preferred. Signed-off-by: Patrick Avery <patrick.avery@kitware.com>
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Profiling the free-energy Hessian of a 128-atom PdH supercell in python-sscha showed 82% of the run inside this method: the Fortran symph routines apply the symmetries element by element in serial.
The three steps are redone with numpy array operations: the permutation symmetry as an average over the 6 index orderings, the translation average computed once per group of equivalent atoms, and each symmetry at gamma applied as one matrix product over the cartesian components.
On the PdH case: 181 s -> 16 s, matching the old result to 1e-16. Also checked on two lower-symmetry cells (120 and 216 atoms). Peak memory grows because of numpy temporaries: on the 216-atom cell about 11 GB against 6.5 GB, with the time going 211 s -> 34 s.
The matrix products use numpy's threaded BLAS but do not depend on it: on the 120-atom cell the Fortran path takes 20.4 s and the numpy one 6.5 s on a single thread (3.8 s with threads on a 24-core machine). On shared nodes without core pinning, cap the threads with OPENBLAS_NUM_THREADS.
The Fortran routines (permute_v3, trans_v3, sym_v3) no longer have callers here and can be removed in a follow-up if preferred.
The other half of this is SSCHAcode/python-sscha#427