ParallelOperations.jl

Basic parallel algorithms for Julia

Features:

User-friendly interface
100% auto-test coverage
All of the operations could be executed on specified Modules
Commonly used operations
Send function methods to remote at runtime

Install

]add ParallelOperations

]add https://github.com/JuliaAstroSim/ParallelOperations.jl

Usage

using Test
using Distributed
addprocs(4)

@everywhere using ParallelOperations

#!!! Notice
# User struct
@everywhere procs() struct TestStruct
    x
    y
end

# Define iterater methods to use REDUCE operations
@everywhere iterate(p::TestStruct) = (p, nothing)
@everywhere iterate(p::TestStruct, st) = nothing

# Functions to execute on remote workers should be known by target worker
@everywhere function f!(a::Array)
    for i in eachindex(a)
        a[i] = sin(a[i])
    end
end

Point-to-point

## Define a variable on worker and get it back
sendto(2, a = 1.0)
b = getfrom(2, :a)
@test b == 1.0

## Specify module (optional)
#!!! Default module is Main
sendto(2, a = 1.0, ParallelOperations)
b = getfrom(2, :a, ParallelOperations)

## Get & Set data by Expr
@everywhere 2 s = TestStruct(0.0, 0.0)
b = 123.0

sendto(2, :(s.x), b)

sendto(2, :(s.y), 456.0)
@everywhere 2 @show s

## Transfer data from worker 2 to worker 3, and change symbol name
transfer(2, 3, :a, :b)
@everywhere 3 @show b

Notice that functions would evaluate the parameter before sending them to remote workers. That means:

sendto(2, a = myid())
b = getfrom(2, :a)

would return b = 1 instead of 2, because function myid is executed on master process.

To send commands to remote, use macros:

@sendto 2 a = myid()
b = getfrom(2, :a)
# b = 2

Here myid is executed on process 2.

This also works with bcast and @bcast (in fact @bcast and @sendto have identical codes)

broadcast

bcast(workers(), :c, 1.0, ParallelOperations)

bcast(workers(), c = [pi/2])

bcast(workers(), f!, :c)

gather

Gathering is executed in the order of the first parameter

d = gather(workers(), :(c[1]))
@test d == 4.0


bcast(pids, a = 1.0)
allgather(pids, :a, :b) # allgather data to new symbol (option)
                        # If ok with unstable type, you could use `allgather(pids, :a)`
b = gather(pids, :b)
@test sum(sum(b)) == 16.0

reduce

@everywhere workers() teststruct = TestStruct(myid(), collect(1:5) .+ myid())
M = reduce(max, workers(), :(teststruct.b))


@everywhere pids a = myid()
allreduce(max, pids, :a) # allreduce data. Use allreduce(max, pids, :a, :b) for new symbol :b
b = gather(pids, :a)
@test sum(b) == 20.0

Scatter

The array to scatter should have the same length as workers to receive

a = collect(1:4)
scatterto(workers(), a, :b, Main)
@everywhere workers() @show b

Commonly used functions

@everywhere workers() x = 1.0

sum(workers(), :x)
allsum(workers(), :x)
maximum(workers(), :x)
allmaximum(workers(), :x)
minimum(workers(), :x)
allminimum(workers(), :x)

Send function to workers

@everywhere fun() = 1

sendto(2, fun)
getfrom(2, :(fun()))

bcast(workers(), fun)
gather(workers(), :(fun()))

Functions with multiple arguments:

@everywhere m(x,y,z) = x+y+z
sendto(2, m, :((1,2,3)...))

Arguments can also be passed by args keyword, which is more user-friendly:

x = 1
sendto(2, m, args = (1,2,3))
sendto(2, m, :($x), args = (2, 3))
gather(m, [1,2], args = (1,2,3))
bcast([1,2], m, args = (1,2,3))

Type-stable

using Distributed
addprocs(1)
@everywhere using ParallelOperations

function testPO()
    @sendto 2 a=5
    a = (@getfrom 2 a)::Int64 # This will restrict the type of a, making both a and b type-stable

    b = a+1
end

function testPOunstable()
    @sendto 2 a=5
    a = @getfrom 2 a

    b = a+1
end

function testPOfun()
    @sendto 2 a=5
    a = (getfrom(2, :a))::Int64 # This will restrict the type of a, making both a and b type-stable

    b = a+1
end

@code_warntype testPO()
@code_warntype testPOunstable()
@code_warntype testPOfun()

TODO

Check remotecall functions
Benchmark and optimization

Similar packages

ParallelDataTransfer

Package ecosystem

Basic data structure: PhysicalParticles.jl
File I/O: AstroIO.jl
Initial Condition: AstroIC.jl
Parallelism: ParallelOperations.jl
Trees: PhysicalTrees.jl
Meshes: PhysicalMeshes.jl
Plotting: AstroPlot.jl
Simulation: ISLENT

JuliaAstroSim / ParallelOperations.jl