Hello,
In the following code, I have tried parallelising the for (j, pair)
loop, however although the results of one iteration should be independent of other iterations, whenever I add Threads.@threads
the result I get changes :frown:
tightnesses = Vector{Float64}(undef, length(𝐑))
besttightness = Inf
@simpleprog length(ixy) * length(𝐑) for (i, x, y) ∈ ixy
D₁ = copy(D)
pairs = D[[x,y],:]
pairs′ = [R * pairs for R ∈ 𝐑]
for (j, pair) ∈ (enumerate(pairs′) |> collect)
proginc!()
D₁[[x,y],:] = pair
𝚡 = basisparameterise(D₁, 𝐱cent𝛟, 𝐱𝛟)
tightnesses[j] = basistightness(𝚡, 𝐱; k, krepr)
end
if minimum(tightnesses) < besttightness
besttightness = minimum(tightnesses)
D[[x,y],:] = pairs′[argmin(tightnesses)]
end
𝐃[:,:,i+1] = copy(D)
end
If anyone has any ideas what's happening and/or how I can get this to work, that would be much appreciated!
For reference length(𝐑)
is 360
.
Many questions arise.
What is ixy
? What is @simpleprog
? What is proginc!
? What is D
? Is there any difference between D
and 𝐃
? What is basisparametrise
? What is 𝐱cent𝛟
? And so on....
Any of this things can potentially make loop iterations-dependent, and it is impossible to say which one just by looking at this code.
One thing, though - you make D1
as a copy of D
, and at the same time you change D
inside iteration (in if minimum...
branch). So, it looks like iterations are not independent.
Unfortunately I the full context is rather ... large. However, I'll attempt to give an idea of what's going on here:
ixy
is a vector of tuples [(1, 1, 2), (2, 1, 3), (3, 1, 4), ..., (26, 2, 13), ..., (240, 15, 16)]
𝐑
is a vector of 2D rotation matrices@simpleprog n body
is a simple macro that generates a progress output for body
which is incremented by proginc!()
D
is just some ortonormal matrix𝐃
is a tensor recording the evolution of D
basisparameterise
is a pure function (i.e. no state) which is completely determanisticbasistightness
is also a pure function, but has a very small random element𝐱cent𝛟
and 𝐱𝛟
are another can of worms, but they're constant valuesRegarding your comment:
One thing, though - you make D1 as a copy of D, and at the same time you change D inside iteration (in if minimum... branch). So, it looks like iterations are not independent.
D
is changed in the outer loop for (i, x, y)
, I'm talking about the inner loop for (j, pair)
.
To further clarify regarding that inner loop:
proginc!()
has no effect on the resultD₁
and tightnesses[j]
are the only values affected by itPerhaps it could help if I make a "reduced" version?
besttightness = Inf
for x ∈ xs, y ∈ ys
D₁ = copy(D)
pairs = D[[x,y],:]
pairs′ = makerotations(pairs)
for (j, pair) ∈ (enumerate(pairs′) |> collect)
D₁[[x,y],:] = pair
tightnesses[j] = somepurefunction(D₁)
end
if minimum(tightnesses) < besttightness
besttightness = minimum(tightnesses)
D[[x,y],:] = pairs′[argmin(tightnesses)]
end
end
I also tried adding D₁ = copy(D)
before D₁[[x,y],:] = pair
at some point, but that didn't improve things.
a minimal working example would be better but definitely it seems that that line is the one causing you problems.
By the way, it seems like the collect
in there is not necessary:
julia> a = [(1,2) (1,3) (1,4)]
1×3 Matrix{Tuple{Int64, Int64}}:
(1, 2) (1, 3) (1, 4)
julia> for (i,j) in enumerate(a)
println(i," ",j)
end
1 (1, 2)
2 (1, 3)
3 (1, 4)
julia> for (i,j) in (enumerate(a) |> collect)
println(i," ",j)
end
1 (1, 2)
2 (1, 3)
3 (1, 4)
Which line are you referring to? Oh, and collect
is needed when using @threads
.
for (j, pair) ∈ (enumerate(pairs′) |> collect)
D₁[[x,y],:] = pair
tightnesses[j] = somepurefunction(D₁)
end
I don't see how this possibly could be parallelized. All threads will try to update the contents of D₁
and will overwrite each other in a data race. You will need to set things up so each thread has its own copy of D₁
to play with.
Last updated: Nov 06 2024 at 04:40 UTC