using cthyb with MPI via python API

[FangXieTHU]

Dear Triqs developers,

So I am trying to use the cthyb library to perform a self-consistent DMFT calculation for benchmark. In a self-consistent calculation, G0_iw in the next iteration is obtained from the self-consistent condition in the current iteration. I am wondering if I have to use if mpi.is_master_node(): S.G0_iw << evaluate_next_G0(S.G_iw) when calculating the next G0_iw. I tried the case without this if statement, which means every cpu is calculating its G0_iw separately, but it seems that the next iteration for different cpus will start at different time. May I ask whether I should only use master_node for self-consistent condition or not? Any help would be appreciated!

Version
triqs 3.1.1
triqs_cthyb 3.1.0

Environment: macOS Ventura on Apple Silicon w/ Rosetta, for single core tests the results are reasonable.

[the-hampel]

Hi @FangXieTHU,

setting S.G0_iw should be done on each mpi rank, because each solver instance (on each rank) has its own input to start with. However, if your function evaluate_next_G0() has some elaborate steps in between that take some time you can of course do this on the master_node only and then bcast the result to all other ranks, i.e. do something like:

if mpi.is_master_node():
  S.G0_iw << evaluate_next_G0(S.G_iw)

S.G0_iw << mpi.bcast(S.G0_iw)
mpi.barrier()

However, now you have to do the extra communication between the nodes. This has to be balanced, depending which task takes how much time.

Best,
Alex

P.S. since this is not really an issue, I moved this to the discussions page.

[FangXieTHU]

Hi @the-hampel ,

Thanks a lot for the reply! Now I am able to obtain reasonable DMFT solutions through triqs_cthyb library. May I also ask if you have any plan of providing the arm64 build binary (and also for triqs library) for the new apple silicon hardwares via conda-forge? Currently it runs without any major issue through Rosetta, but the performance is a bit limited.

Best,
Fang

[the-hampel]

Hi,
Perfect. I think for now there are no specific plans for providing arm64 build binaries. We will definitely think about it.
Best,
Alex

[Wentzell]

@FangXieTHU triqs packages for version 3.2 as well as the applications triqs_cthyb, triqs_tprf and triqs_dft_tools are now available through anaconda for ARM based Macs.