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
]add ParallelOperationsor
]add https://github.com/JuliaAstroSim/ParallelOperations.jlusing 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## 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 bNotice 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 = 2Here myid is executed on process 2.
This also works with bcast and @bcast (in fact @bcast and @sendto have identical codes)
bcast(workers(), :c, 1.0, ParallelOperations)
bcast(workers(), c = [pi/2])
bcast(workers(), f!, :c)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@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) == 20.0The 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@everywhere workers() x = 1.0
sum(workers(), :x)
allsum(workers(), :x)
maximum(workers(), :x)
allmaximum(workers(), :x)
minimum(workers(), :x)
allminimum(workers(), :x)@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))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()- Check remotecall functions
- Benchmark and optimization
- 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
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