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comparison distributedTest.jl @ 146:21b188f38358 parallel_test

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Started branch for testing out parallel computing
author Vidar Stiernström <vidar.stiernstrom@it.uu.se>
date Mon, 25 Feb 2019 16:36:15 +0100
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children aa18b7bf4926
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141:18b3c63673b3 146:21b188f38358
1 @everywhere using Distributed
2 @everywhere using DistributedArrays
3
4 # TODO: Currently uses integer division to calculate the local grid size.
5 # Should we make sure this is handled in some way if mod(sz./nworkers()) != 0
6 # or keep assertions?
7 @everywhere function create_partitioned_grid(size::NTuple{Dim, Int}, limit_lower::NTuple{Dim, T}, limit_upper::NTuple{Dim, T}, nworkers_per_dim::NTuple{Dim, Int}) where Dim where T
8 @assert mod.(size, nworkers_per_dim) == (0,0)
9 @assert prod(nworkers_per_dim) == nworkers()
10 # Translate the current worker id to a cartesian index, based on nworkers_per_dim
11 ci = CartesianIndices(nworkers_per_dim);
12 id = Tuple(ci[myid()-1])
13
14 # Compute the size of each partitioned grid
15 size_partition = size./nworkers_per_dim
16 size_partition = map(x->Int(x),size_partition) # TODO: Cant this be done in an easier way?...
17 # Compute domain size for each partition
18 domain_size = limit_upper.-limit_lower
19 domain_partition_size = domain_size./nworkers_per_dim
20
21 # Compute the lower and upper limit for each grid partition, then construct the grid
22 ll_partition = limit_lower .+ domain_partition_size.*(id.-1)
23 lu_partition = limit_lower .+ domain_partition_size.*id
24 grid = sbp.Grid.EquidistantGrid(size_partition, ll_partition, lu_partition)
25 return grid
26 end
27
28 # Create grid
29 gridsize = (10000, 10000);
30 limit_lower = (0., 0.)
31 limit_upper = (2pi, 3pi/2)
32 # TODO: Currently only works with same number of processes in each direction and for
33 # an even number of processes
34 nworkers_per_dim = (Int(nworkers()/2),Int(nworkers()/2))
35 grids_partitioned = [@spawnat p create_partitioned_grid(gridsize, limit_lower , limit_upper, nworkers_per_dim) for p in workers()]
36
37 # Create Laplace operator
38 @everywhere op = sbp.readOperator("d2_4th.txt","h_4th.txt")
39 Laplace_partitioned = [@spawnat p sbp.Laplace(fetch(grids_partitioned[p-1]), 1.0, op) for p in workers()]
40
41 # Create initial value grid function v and solution grid function u
42 # TODO: u and v could be a distributed arrays from the start.
43 # Then pass local parts of the distributed arrays to the functions
44 @everywhere init(x,y) = sin(x) + sin(y)
45 v = [@spawnat p sbp.Grid.evalOn(fetch(grids_partitioned[p-1]), init) for p in workers()]
46 u = [@spawnat p zero(fetch(v[p-1])) for p in workers()]
47
48 # # Apply Laplace
49 fetch([@spawnat p sbp.apply_tiled!(fetch(Laplace_partitioned[p-1]),fetch(u[p-1]), fetch(v[p-1])) for p in workers()])
50
51 # Construct global vector and store in distributed array
52 u = reshape(u,(2,2))
53 u_global = DArray(u)
54 v = reshape(v,(2,2))
55 v_global = DArray(v)
56 @show maximum(abs.(u_global + v_global))