This section summarizes the options DFTK offers to monitor and influence performance of the code.
By default DFTK uses TimerOutputs.jl to record timings, memory allocations and the number of calls for selected routines inside the code. These numbers are accessible in the object
DFTK.timer. Since the timings are automatically accumulated inside this datastructure, any timing measurement should first reset this timer before running the calculation of interest.
For example to measure the timing of an SCF:
DFTK.reset_timer!(DFTK.timer) scfres = self_consistent_field(basis, tol=1e-8) DFTK.timer
──────────────────────────────────────────────────────────────────────────────── Time Allocations ─────────────────────── ──────────────────────── Tot / % measured: 1.14s / 28.3% 95.0MiB / 40.2% Section ncalls time %tot avg alloc %tot avg ──────────────────────────────────────────────────────────────────────────────── self_consistent_field 1 323ms 99.9% 323ms 37.9MiB 99.1% 37.9MiB LOBPCG 15 181ms 56.0% 12.1ms 12.7MiB 33.3% 869KiB ortho! X vs Y 65 69.2ms 21.4% 1.07ms 1.65MiB 4.3% 25.9KiB ortho! 135 64.1ms 19.8% 475μs 1.04MiB 2.7% 7.85KiB DftHamiltonian... 55 57.4ms 17.8% 1.04ms 4.38MiB 11.5% 81.5KiB local+kinetic 352 53.1ms 16.4% 151μs 275KiB 0.7% 800B nonlocal 55 1.88ms 0.6% 34.2μs 0.99MiB 2.6% 18.4KiB ortho! 15 32.3ms 10.0% 2.16ms 93.3KiB 0.2% 6.22KiB rayleigh_ritz 40 13.4ms 4.2% 336μs 1.27MiB 3.3% 32.4KiB preconditioning 55 885μs 0.3% 16.1μs 35.9KiB 0.1% 669B energy_hamiltonian 11 78.6ms 24.3% 7.14ms 17.8MiB 46.6% 1.62MiB ene_ops 11 69.9ms 21.6% 6.36ms 11.5MiB 30.2% 1.05MiB ene_ops: xc 11 59.6ms 18.4% 5.42ms 5.19MiB 13.6% 483KiB ene_ops: har... 11 8.21ms 2.5% 746μs 4.96MiB 13.0% 462KiB ene_ops: non... 11 990μs 0.3% 90.0μs 163KiB 0.4% 14.8KiB ene_ops: kin... 11 445μs 0.1% 40.4μs 103KiB 0.3% 9.35KiB ene_ops: local 11 303μs 0.1% 27.6μs 1.01MiB 2.6% 93.8KiB compute_density 5 58.9ms 18.2% 11.8ms 3.47MiB 9.1% 710KiB symmetrize_ρ 5 49.8ms 15.4% 10.0ms 2.78MiB 7.3% 570KiB accumulate_o... 5 48.6ms 15.0% 9.73ms 338KiB 0.9% 67.6KiB QR orthonormaliz... 15 288μs 0.1% 19.2μs 238KiB 0.6% 15.8KiB χ0Mixing 5 60.8μs 0.0% 12.2μs 20.5KiB 0.1% 4.11KiB guess_density 1 483μs 0.1% 483μs 366KiB 0.9% 366KiB ────────────────────────────────────────────────────────────────────────────────
The output produced when printing or displaying the
DFTK.timer now shows a nice table summarising total time and allocations as well as a breakdown over individual routines.
Timing measurements have the unfortunate disadvantage that they alter the way stack traces look making it sometimes harder to find errors when debugging. For this reason timing measurements can be disabled completely (i.e. not even compiled into the code) by setting the environment variable
"false". For this to take effect recompiling all DFTK (including the precompile cache) is needed.
A very (very) rough estimate of the time per SCF step (in seconds) can be obtained with the following function. The function assumes that FFTs are the limiting operation and that no parallelisation is employed.
function estimate_time_per_scf_step(basis::PlaneWaveBasis) # Super rough figure from various tests on cluster, laptops, ... on a 128^3 FFT grid. time_per_FFT_per_grid_point = 30 #= ms =# / 1000 / 128^3 (time_per_FFT_per_grid_point * prod(basis.fft_size) * length(basis.kpoints) * div(basis.model.n_electrons, DFTK.filled_occupation(basis.model), RoundUp) * 8 # mean number of FFT steps per state per k-point per iteration ) end "Time per SCF (s): $(estimate_time_per_scf_step(basis))"
"Time per SCF (s): 0.008009033203124998"
At the moment DFTK offers two ways to parallelize a calculation, firstly shared-memory parallelism using threading and secondly multiprocessing using MPI (via the MPI.jl Julia interface). MPI-based parallelism is currently only over $k$-points, such that it cannot be used for calculations with only a single $k$-point. Otherwise combining both forms of parallelism is possible as well.
The scaling of both forms of parallelism for a number of test cases is demonstrated in the following figure. These values were obtained using DFTK version 0.1.17 and Julia 1.6 and the precise scalings will likely be different depending on architecture, DFTK or Julia version. The rough trends should, however, be similar.
The MPI-based parallelization strategy clearly shows a superior scaling and should be preferred if available.
Currently DFTK uses MPI to distribute on $k$-points only. This implies that calculations with only a single $k$-point cannot use make use of this. For details on setting up and configuring MPI with Julia see the MPI.jl documentation.
First disable all threading inside DFTK, by adding the following to your script running the DFTK calculation:
using DFTK disable_threading()
Run Julia in parallel using the
mpiexecjlwrapper script from MPI.jl:
mpiexecjl -np 16 julia myscript.jl
-np 16tells MPI to use 16 processes and
-t 1tells Julia to use one thread only. Notice that we use
mpiexecjlto automatically select the
mpiexeccompatible with the MPI version used by MPI.jl.
As usual with MPI printing will be garbled. You can use
DFTK.mpi_master() || (redirect_stdout(); redirect_stderr())
at the top of your script to disable printing on all processes but one.
While standard procedures (such as the SCF or band structure calculations) fully support MPI, not all routines of DFTK are compatible with MPI yet and will throw an error when being called in an MPI-parallel run. In most cases there is no intrinsic limitation it just has not yet been implemented. If you require MPI in one of our routines, where this is not yet supported, feel free to open an issue on github or otherwise get in touch.
Threading in DFTK currently happens on multiple layers distributing the workload over different $k$-points, bands or within an FFT or BLAS call between threads. At its current stage our scaling for thread-based parallelism is worse compared MPI-based and therefore the parallelism described here should only be used if no other option exists. To use thread-based parallelism proceed as follows:
Ensure that threading is properly setup inside DFTK by adding to the script running the DFTK calculation:
using DFTK setup_threading()
This disables FFT threading and sets the number of BLAS threads to the number of Julia threads.
Run Julia passing the desired number of threads using the flag
julia -t 8 myscript.jl
For some cases (e.g. a single $k$-point, fewish bands and a large FFT grid) it can be advantageous to add threading inside the FFTs as well. One example is the Caffeine calculation in the above scaling plot. In order to do so just call
setup_threading(n_fft=2), which will select two FFT threads. More than two FFT threads is rarely useful.
The default threading setup done by
setup_threading is to select one FFT thread and the same number of BLAS and Julia threads. This section provides some info in case you want to change these defaults.
All BLAS calls in Julia go through a parallelized OpenBlas or MKL (with MKL.jl. Generally threading in BLAS calls is far from optimal and the default settings can be pretty bad. For example for CPUs with hyper threading enabled, the default number of threads seems to equal the number of virtual cores. Still, BLAS calls typically take second place in terms of the share of runtime they make up (between 10% and 20%). Of note many of these do not take place on matrices of the size of the full FFT grid, but rather only in a subspace (e.g. orthogonalization, Rayleigh-Ritz, ...) such that parallelization is either anyway disabled by the BLAS library or not very effective. To set the number of BLAS threads use
using LinearAlgebra BLAS.set_num_threads(N)
N is the number of threads you desire. To check the number of BLAS threads currently used, you can use
Int(ccall((BLAS.@blasfunc(openblas_get_num_threads), BLAS.libblas), Cint, ()))
or (from Julia 1.6) simply
On top of BLAS threading DFTK uses Julia threads (
Thread.@threads) in a couple of places to parallelize over $k$-points (density computation) or bands (Hamiltonian application). The number of threads used for these aspects is controlled by the flag
-t passed to Julia or the environment variable
JULIA_NUM_THREADS. To check the number of Julia threads use
Since FFT threading is only used in DFTK inside the regions already parallelized by Julia threads, setting FFT threads to something larger than
1 is rarely useful if a sensible number of Julia threads has been chosen. Still, to explicitly set the FFT threads use
using FFTW FFTW.set_num_threads(N)
N is the number of threads you desire. By default no FFT threads are used, which is almost always the best choice.