Dear developers,
I tried to use -ndiag options in scf process. Because I read tutorials which says "most CPU time spent in linear-algebra operations, implemented in BLAS and LAPACK libraries, and in FFT". And what linear algebra parallelization does is to "Distribute and parallelize matrix diagonalization and matrix- matrix multiplications needed in iterative diagonalization (SCF) ". So I thought adding -ndiag should have an obvious speedup. To test, I artificially construct a 2x2 supercell of copper which contains 8 atoms, and run
mpiexec.hydra -n 12 pw.x -ndiag 9 -in cu_supercell.scf.in > cu_supercell.scf.out
But I didn't see any improvement over no -ndiag run. The timing is almost the same. I check the output file, it does show that
Subspace diagonalization in iterative solution of the eigenvalue problem:
one sub-group per band group will be used
scalapack distributed-memory algorithm (size of sub-group: 3* 3 procs)
I also test a more practical case with a material of 23 atoms in primitive cell, and see no improvement. So I am wondering what is wrong? Is -ndiag not effective for scf?
best regards
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