Advanced Usage: Running in parallel

To simplify the parallelization of algorithms when using the package, we export some preliminary methods based on the MPI.jl wrapper. For further information on how to set up MPI with Julia see https://github.com/JuliaParallel/MPI.jl.

using MatsubaraFunctions 
using MPI 

MPI.Init()
mpi_info()
mpi_println("I print on main.")
ismain = mpi_ismain() # ismain = true if rank is 0

T = 1.0
N_= 128
g = MatsubaraGrid(T, N_, Fermion)
f = MatsubaraFunction(g, 1)

# simple loop parallelization for UnitRange
for vidx in mpi_split(1 : length(g))
    println("My rank is $(mpi_rank()): $(vidx)")
end

# simple (+) allreduce
mpi_allreduce!(f)

In addition, calls of MatsubaraSymmetryGroup with an initialization function have an opt-in switch (mode) to enable parallel evaluation of the MatsubaraInitFunction (by default mode = :serial). If mode = :polyester, shared memory multithreading via the Polyester (https://github.com/JuliaSIMD/Polyester.jl) Julia package is used. This mode is recommended for initialization functions that are cheap to evaluate and are unlikely to benefit from Threads.@threads due to the overhead from invoking the Julia scheduler. For more expensive functions, users can choose between mode = :threads, which simply uses Threads.@threads, and mode = :hybrid. The latter combines both MPI and native Julia threads and can therefore be used to run calculations on multiple compute nodes.

Functions

function mpi_comm() :: MPI.Comm

function mpi_rank() :: Int64

function mpi_size() :: Int64

function mpi_split(
    r :: UnitRange{Int64}
    ) :: UnitRange{Int64}

function mpi_allreduce!(
    f :: MatsubaraFunction{GD, SD, DD, Q}
    ) :: Nothing where {GD, SD, DD, Q <: Number}

mpi_ismain() :: Bool

function mpi_println(
    s :: String
    ) :: Nothing

mpi_info() :: Nothing

mpi_barrier() :: Nothing