I would like to calculate a quadratic form: x' Q y in Julia.
What would be the most efficient way to calculate this for the cases:
No assumption.
Q is symmetric.
x and y are the same (x = y).
Both Q is symmetric and x = y.
I know Julia has dot(). But I wonder if it is faster than BLAS call.
The existing answers are both good. However, a few additional points:
While Julia will indeed by default simply call BLAS here, as Oscar notes, some BLASes are faster than others. In particular, MKL will often be somewhat faster than OpenBLAS on modern x86 hardware. Fortunately, in Julia it is unusually easy to manually choose your BLAS backend. Simply typing using MKL at the REPL on Julia 1.7 or later will switch you to the MKL backend via the MKL.jl package.
While it is not used by default, there do now exist a couple pure Julia linear algebra packages that can match or even beat traditional Fortran-based BLAS. In particular, Octavian.jl:
Julia's LinearAlgebra stdlib has native implementations of 3-argument dot, and also a version that is specialized for symmetric/hermitian matrices. You can view the source here and here.
You can confirm that they do not allocate using BenchmarkTools.#btime or BenchmarkTools.#ballocated (remember to interpolate variables using $). The symmetry of the matrix is exploited, but looking at the source, I don't see how x == y could enable any serious speedup, except perhaps saving a few array lookups.
Edit: To compare the execution speed of the BLAS version and the native one, you can do
1.7.0> using BenchmarkTools, LinearAlgebra
1.7.0> X = rand(100,100); y=rand(100);
1.7.0> #btime $y' * $X * $y
42.800 μs (1 allocation: 896 bytes)
1213.5489200642382
1.7.0> #btime dot($y, $X, $y)
1.540 μs (0 allocations: 0 bytes)
1213.548920064238
This is a big win for the native version. For bigger matrices, the picture changes, though:
1.7.0> X = rand(10000,10000); y=rand(10000);
1.7.0> #btime $y' * $X * $y
33.790 ms (2 allocations: 78.17 KiB)
1.2507105095988091e7
1.7.0> #btime dot($y, $X, $y)
44.849 ms (0 allocations: 0 bytes)
1.2507105095988117e7
Possibly because BLAS uses threads, while dot is not multithreaded. There are also some floating point differences.
If your matrix is symmetric use the Symmetric wrapper to improve performance (a different method is called then):
julia> a = rand(10000); b = rand(10000);
julia> x = rand(10000, 10000); x = (x + x') / 2;
julia> y = Symmetric(x);
julia> #btime dot($a, $x, $b);
47.000 ms (0 allocations: 0 bytes)
julia> #btime dot($a, $y, $b);
27.392 ms (0 allocations: 0 bytes)
If x is the same as y see https://discourse.julialang.org/t/most-efficient-way-to-compute-a-quadratic-matrix-form/66606 for a discussion of options (but in general it seems dot is still fast then).
You can write an optimized loop using Tullio.jl, doing this all in one sweep. But I think it's not going to beat BLAS significantly:
Chained multiplication is also very slow, because it doesn't know there's a better algorithm.
julia> # a, b, x are the same as in Bogumił's answer
julia> #btime dot($a, $x, $b);
82.305 ms (0 allocations: 0 bytes)
julia> f(a, x, b) = #tullio r := a[i] * x[i,j] * b[j]
f (generic function with 1 method)
julia> #btime f($a, $x, $b);
80.430 ms (1 allocation: 16 bytes)
Adding LoopVectorization.jl may be worth it:
julia> using LoopVectorization
julia> f3(a, x, b) = #tullio r := a[i] * x[i,j] * b[j]
f3 (generic function with 1 method)
julia> #btime f3($a, $x, $b);
73.239 ms (1 allocation: 16 bytes)
But I don't know about how to go about the symmetric case.
julia> #btime dot($a, $(Symmetric(x)), $b);
42.896 ms (0 allocations: 0 bytes)
although there might be linear algebra tricks to cut that down intelligently with Tullio.jl.
In this kind of problem, benchmarking trade-offs is everything.
Related
I am trying to minimize memory allocations in Julia by pre-allocating arrays as shown in the documentation. My sample code looks as follows:
using BenchmarkTools
dim1 = 100
dim2 = 1000
A = rand(dim1,dim2)
B = rand(dim1,dim2)
C = rand(dim1,dim2)
D = rand(dim1,dim2)
M = Array{Float64}(undef,dim1,dim2)
function calc!(a, b, c, d, E)
#. E = a * b * ((d-c)/d)
nothing
end
function run_calc(A,B,C,D,M)
for i in 1:dim2
#views calc!(A[:,i], B[:,i], C[:,i], D[:,i], M[:,i])
end
end
My understanding is that this should essentially not allocate since M is pre-allocated outside the either of the two functions. However, when I benchmark this I still see a lot of allocations:
#btime run_calc(A,B,C,D,M)
1.209 ms (14424 allocations: 397.27 KiB)
In this case I can of course run the much more concise
#btime #. M = A * B * ((D-C)/D)
which performs very few allocations as expected:
122.599 μs (6 allocations: 144 bytes)
However my actual code is more complex and cannot be reduced like this, hence I am wondering where I am going wrong with the first version.
You are not doing anything wrong. Currently creation of views in Julia is allocating (as Stefan noted it has gotten much better than in the past, but still some allocations seem to happen in this case). The allocations you see are a consequence of this.
See:
julia> #allocated view(M, 1:10, 1:10)
64
Your case is one of the situations where it is simplest to just write an appropriate loop (I assume that in your code the loop will be more complex but I hope the intent is clear), e.g.:
julia> function run_calc2(A,B,C,D,M)
#inbounds for i in eachindex(A,B,C,D,M)
M[i] = A[i] * B[i] * ((D[i] - C[i])/D[i])
end
end
run_calc2 (generic function with 1 method)
julia> #btime run_calc2($A,$B,$C,$D,$M)
56.441 μs (0 allocations: 0 bytes)
julia> #btime run_calc($A,$B,$C,$D,$M)
893.789 μs (14424 allocations: 397.27 KiB)
julia> #btime #. $M = $A * $B * (($D-$C)/$D);
381.745 μs (0 allocations: 0 bytes)
EDIT: all timings on Julia Version 1.6.0-DEV.1580
EDIT2: for completeness a code that passes #views down to the inner function. It still allocates (but is better) and is still slower than using just the loop:
julia> function calc2!(a, b, c, d, E, i)
#inbounds #. #views E[:,i] = a[:,i] * b[:,i] * ((d[:,i]-c[:,i])/d[:,i])
nothing
end
calc2! (generic function with 1 method)
julia> function run_calc3(A,B,C,D,M)
for i in 1:dim2
calc2!(A,B,C,D,M,i)
end
end
run_calc3 (generic function with 1 method)
julia> #btime run_calc3($A,$B,$C,$D,$M);
305.709 μs (1979 allocations: 46.56 KiB)
Prior to Julia 1.5, creating array views will often allocate a bit of memory for the view object. After Julia 1.5, creating views will usually not cause any allocation. Your post doesn't include what version of Julia you're using, so I'll assume that it's older than 1.5. In your code, you are creating a view for each index of a potentially large array dimension, which will definitely add up. You could refactor this code to pass the dimension through to the inner calculation. Otherwise you can upgrade Julia and see if the allocation goes away.
I have a stack of images (3D array) and I want to improve their resolution (upsampling). I run the following code snippet that I find a little slow ...
Is there any way to improve the speed of this piece of code? (without using multiprocessing)
using BenchmarkTools
using Interpolations
function doInterpol(arr::Array{Int, 2}, h, w)
A = interpolate(arr, BSpline(Linear()))
return A[1:2/(h-1)/2:2, 1:2/(w-1)/2:2]
end
function applyResize!(arr3D_hd::Array, arr3D_ld::Array, t::Int, h::Int, w::Int)
for i = 1:1:t
#inbounds arr3D_hd[i, :, :] = doInterpol(arr3D_ld[i, :, :], h, w)
end
end
t, h, w = 502, 65, 47
h_target, w_target = 518, 412
arr3D_ld = reshape(collect(1:t*h*w), (t, h, w))
arr3D_hd = Array{Float32}(undef, t, h_target, w_target)
applyResize!(arr3D_hd, arr3D_ld, t, h_target, w_target)
When I benchmark the following:
#btime applyResize!(arr3D_hd, arr3D_ld, t, h_target, w_target)
I got :
2.334 s (68774 allocations: 858.01 MiB)
I ran it multiple time and results are in [1.8s - 2.8s] interval.
Julia stores arrays in column-major order. This means that slices like arr[i, : ,:] perform much worse than arr[:,:,i] (which is contiguous in memory). Therefore, a way to gain some speed is to index your arrays using (h,w,t) rather than (t, w, h).
A second issue is that taking slices like arr[i,:,:] copies data. It seems to have negligible impact here, but it might be good to get into the habit of using array views instead of slices when you can. A view is a small wrapper object that behaves in the same way as a slice of a larger array, but does not hold a copy of the data: it directly accesses the data of the parent array (see the example below to maybe better understand what a view is).
Note that both these issues are mentioned in the Julia performance tips; it might be useful to read the remaining pieces of advice in this page.
Putting this together, your example can be rewritten like:
function applyResize2!(arr3D_hd::Array, arr3D_ld::Array, h::Int, w::Int, t)
#inbounds for i = 1:1:t
A = interpolate(#view(arr3D_ld[:, :, i]), BSpline(Linear()))
arr3D_hd[:, :, i] .= A(1:2/(h-1)/2:2, 1:2/(w-1)/2:2)
end
end
which is used with arrays stored a bit differently from your case:
# Note the order of indices
julia> arr3D_ld = reshape(collect(1:t*h*w), (h, w, t));
julia> arr3D_hd = Array{Float32}(undef, h_target, w_target, t);
# Don't forget to escape arguments with a $ when using btime
# (not really an issue here, but could have been one)
julia> #btime applyResize2!($arr3D_hd, $arr3D_ld, h_target, w_target, t)
506.449 ms (6024 allocations: 840.11 MiB)
This is roughly a speed-up by a factor 3.4 w.r.t your original code, which benchmarks like this on my machine:
julia> arr3D_ld = reshape(collect(1:t*h*w), (t, h, w));
julia> arr3D_hd = Array{Float32}(undef, t, h_target, w_target);
julia> #btime applyResize!($arr3D_hd, $arr3D_ld, t, h_target, w_target)
1.733 s (50200 allocations: 857.30 MiB)
NB: Your original code uses a syntax like A[x, y] to get interpolated values. This seems to be deprecated in favor of A(x, y). I might not have the same version of Interpolations as you, though...
Example illustrating the behavior of views
julia> a = rand(3,3)
3×3 Array{Float64,2}:
0.042097 0.767261 0.0433798
0.791878 0.764044 0.605218
0.332268 0.197196 0.722173
julia> v = #view(a[:,2]) # creates a view instead of a slice
3-element view(::Array{Float64,2}, :, 2) with eltype Float64:
0.7672610491393876
0.7640443797187411
0.19719581867637093
julia> v[3] = 42 # equivalent to a[3,2] = 42
42
Use
itp = interpolate(arr3D_ld, (NoInterp(), BSpline(Linear()), BSpline(Linear())));
A = itp(1:size(itp,1), 1:2/517:2, 1:2/411:2);
It should give a ~7x performance improvement compared to your version.
As François Févotte noted, it's also important to pay attention to deprecation warnings, as they slow down execution.
I have some rolling function which evaluates an array according to a function,
rolling(x, func, window=10) = [func(x[1+i:window+i+1]) for i in 0:(length(x)-window-1)]
for instance for the mean:
julia> using Statistics
julia> rolling(1:10,mean,5)
5-element Array{Float64,1}:
3.5
4.5
5.5
6.5
7.5
I wonder if there was an easy and fast and safe way to parallelize this function?
Here it is. Note that for mean it could be done differently because of its rolling properties. Performance will also vary. For smaller loops sometimes #simd could be better (strongly depends on the actual scenario. Hence consider this a starting point.
using Statistics
rolling0(x, func, window=10) = [func(#view x[1+i:window+i+1]) for i in 0:(length(x)-window-1)]
function rolling(x, func, window=10)
res = Vector{Float64}(undef, length(x)-window)
Threads.#threads for i in 1:(length(x)-window)
#inbounds res[i] = func(#view x[i:i+window])
end
res
end
z = rand(10000)
#assert rolling0(z,mean) == rolling(z,mean)
using BenchmarkTools
Tests:
julia> #btime rolling0($z, mean);
104.601 μs (9994 allocations: 546.48 KiB)
julia> #btime rolling($z, mean);
48.100 μs (10021 allocations: 549.88 KiB)
I have a simple function which appears at several places in my Julia code and is run millions of times inside a loop. The function essentially does rand([1,-1,im,-im]), that's, it picks one of four possible given values. I noticed that this function takes a substantial amount of time in my huge loop, so, I tried to write it in a slightly faster way like this:
function qpsk()
temp1 = ifelse(rand(Bool), 1+0im, -1+0im)
temp2 = ifelse(rand(Bool), 1+0im, 0+1im)
temp1*temp2
end
Then, it is typically called like this:
sig = complex(zeros(N))
for i = 1:N
sig[i] = qpsk()
end
Now, is there any way to further optimize this function, or use another faster method? Appreciate your help.
Comments on current answers:
The answer of #DanGetz (22 lines??) doesn't solve the problem, because at the moment, Julia is not as good at vectors as with explicit loops. Also,
my simple, 1 line qpsk2(s) below, is about 2X faster than those "cryptic" 22 lines of code in the original answer by Dan (a vector is created, though, which adds more time).
But the question remains, why they
didn't implement something like qpsk1 below? and why my original qpsk with branching is more than 3X faster than the straightforward qpsk4(s) below?
I added more versions below to guide the discussion if more experienced people like to jump in.
qpsk1(s) = s[1+(rand(Int8)&3)] # Blazingly fast
qpsk2(s) = s[1+rand(Bool)+2rand(Bool)] # Very fast
qpsk3(s) = s[rand(1:4,1)] # Compiler issue here?
qpsk4(s) = s[rand(1:4)] # Why slow?
qpsk5(s) = rand([s]) # Ridiculously slow!!
function test_orig(n) # Test qpsk(), very fast(branching!), why?
for i = 1:n
qpsk()
end
end
using StaticArrays
function test(func, n) # Test all qpsk1 --> qpsk5
s = SVector(1,-1,im,-im)
for i=1:n
func(s)
end
end
#time test(qpsk1,10^8) 0.554994 seconds (5 allocations: 176 bytes)
#time test(qpsk2,10^8) 0.755286 seconds (5 allocations: 176 bytes)
#time test(qpsk3,10^8) 13.431529 seconds (400 M allocations: 26.822 GiB, 20.68% gc time)
#time test(qpsk4,10^8) 2.520085 seconds (5 allocations: 176 bytes)
#time test(qpsk5,10^8) 10.881852 seconds (200 M allocations: 20.862 GiB, 19.76% gc time)
#time test_orig(10^8) 0.771778 seconds (5 allocations: 176 bytes)
#time nqpsk2(10^8); 1.402830 seconds (9 allocations: 1.490 GiB, 6.39% gc time)
Summary of answer
[(-1)^b1*im^b2 for (b1,b2) in zip(rand!(BitVector(N)),rand!(BitVector(N)))]
generates a length N vector faster.
Answer
Calculating the random bits is the bulk of the work, so exploring Chris' idea from comments of using RandomNumbers.jl is worth a shot. Additionally, we can use #rickhg12hs's idea to extract more bits from each random number generated. Regardless, generating a block of values together is essential for better optimization.
For example, the following code (nqpsk1 uses qpsk from the question as the baseline. nqpsk2 is a suggested improvement):
function qpsk()
temp1 = ifelse(rand(Bool), 1+0im, -1+0im)
temp2 = ifelse(rand(Bool), 1+0im, 0+1im)
temp1*temp2
end
nqpsk1(n::Int) = [qpsk() for i=1:n]
nqpsk2(n::Int) = begin
res = zeros(Int,2*n)
blocks = n >>> 4 # use blocks of 16 values
btail = n & 0x000000000000000f # in case n is not a multiple of 16
pos = 1
#inbounds for i=1:blocks
bits = rand(UInt32) # get random bits for a whole block
for j=1:16
b1 = Bool(bits & 1)
bits >>>= 1
b2 = Bool(bits & 1)
bits >>>= 1
res[pos+b1] = (-1)^b2
pos += 2
end
end
#inbounds for i=1:btail
res[pos+rand(Bool)] = (-1)^rand(Bool)
pos += 2
end
return reinterpret(Complex{Int64},res)
end
achieved a >4x improvement on my setup (Julia 0.7):
julia> using BenchmarkTools
julia> #btime nqpsk1(320);
8.791 μs (323 allocations: 15.19 KiB)
julia> #btime nqpsk2(320);
1.056 μs (3 allocations: 5.20 KiB)
Update
With only a modest compromise in speed (and some allocation), but much better looking code:
function nqpsk3(n::Int)
res = zeros(Int,2n)
rv1 = rand!(BitVector(n))
rv2 = rand!(BitVector(n))
#inbounds for (b1,b2,i) in zip(rv1,rv2,1:2:2n)
res[i+b1] = (-1)^b2
end
return reinterpret(Complex{Int},res)
end
The benchmark:
julia> #btime nqpsk3(320);
1.780 μs (11 allocations: 5.83 KiB)
Addendum
And the one-(wrapped)-line version, does OK (2.48 μs) too:
nqpsk4(n) = [(1+0im,-1+0im,0+im,0-im)[2b1+b2+1] for
(b1,b2) in zip(rand!(BitVector(n)),rand!(BitVector(n)))]
Finally, the real one-line version (1.96 μs):
nqpsk5(n) = [(-1)^b1*im^b2 for (b1,b2) in zip(rand!(BitVector(n)),rand!(BitVector(n)))]
Latest state of investigation
My current best solution is the following:
function g(pX::Array{Complex{Float64},1})
tab = [1.0,im,-1.0,-im]
bits = UInt128(0)
#inbounds for i = 1 : length(pX)
bits = (i % 64) == 1 ? rand(UInt128) : bits >>> 2
pX[i] = tab[(bits & 3)+1]
end
end
sig = complex(zeros(1280));
using BenchmarkTools
#btime g(sig)
3.838 μs (13 allocations: 464 bytes)
This is better than my optimized version of Dan Getz which runs with the same N, and i feel much more readable
4.236 μs (4 allocations: 20.16 KiB)
However, the performance is extremely fragil. Just have a look at subtle differences to this 36 times slower version:
function g(pX::Array{Complex{Float64},1})
tab = [1,im,-1,-im]
bits = 0
for i = 1 : length(pX)
bits = (i % 64) == 1 ? rand(UInt128) : bits >>> 2
pX[i] = tab[(bits & 3)+1]
end
end
138.320 μs (10209 allocations: 319.14 KiB)
Did you find the differences?
no conversion from Int64 to Float64
Type stability
disable range checking
to follow the convention g() should be renamed to g!()
In the following you find the evolution to the currently best timed solution
My first approach to answer was addressing general weaknesses
a) calling functions is expensive due to calling overhead.
b) complex calculations are more time consuming than lookup.
This end up with the proposal
cases = [1+0im,0+1im,-1+0im,0-1im]
g() = cases[rand(1:4)]
// to use just call g()
g()
What happend?
Why does a) do not succeed?
using BenchmarkTools
test(n) = [q() for i = 1:n]
g() = rand()
#btime test(800);
This results to
rand() => 5.784
rand(Float32) => 5.604
rand(Float64) => 5.821
rand(Bool) => 5.167
rand(Int8) => 5.126
rand(Int16) => 5.171
rand(Int32) => 5.631
rand(Int64) => 7.980
rand(Int128) => 10.549
rand(1:4) => 28.603
(rand(Int8) % 4) + 1 => 6.053
(rand(Int8) & 3) + 1 => 5.843
rand(0:255) => 28.568
rand(UInt8) => 5.104
rand([1,2,3,4]) => 58.437
l = [1,2,3,4]; g() = rand(l) => 47.399
rand(l, 1) => 70.052
m = (1,2,3,4); rand(m) => 124.311
0 => 0.872
0.0 => 0.887
Int8(0) => 0.113
return => 0.33
(running Julia 0.6 on Ubuntu)
How to judge the results
requesting float32 and float64 needs same time. This may be an indicator that float64 does NOT the full mantisse (of 56 bits) for random value
rand for Bool, Int8, Int16 needs nearly the same time. Probably same algorithmus just using fewer bits.
rand for Int32 slightly more time consuming. Int64 and Int128 takes under proportional more time.
rand(1:4) takes surprisingly much more time. It should be in the range of rand(Int8) since it is equivalent to (rand(Int8) % 4) + 1 and (rand(Int8) & 3) + 1.
Even if I hurt somebodies religious feelings, this is just poor code.
Same to rand(Uint) and rand(0:255)
The performance of rand with arrays and tupel is far from acceptable!
Why does b) do not succeed?
Julia seems not able to lookup efficiently from tupels or arrays.
But even when lookup were fast, the rand methods dominates.
Other approaches
Dan Getz approach uses all bits from a rand call. So in end it needs in his first algorithm 1/16 calls per value.
However, this approach could be improved by using UInt128 since now 1/64 calls per value is required.
On my machine Dan Getz original code takes 17.314 for 1280 values while the modified code takes 4.595. The improvement is proportional to the reduced number of calls to rand!
test2(n::Int) = begin
res = zeros(Int,2*n)
blocks = n >>> 7 # use blocks of 16 values
btail = n & 0x000000000000007f # in case n is not a multiple of 16
pos = 1
#inbounds for i=1:blocks
bits = rand(UInt128) # get random bits for a whole block
for j=1:16
b1 = Bool(bits & 1)
bits >>>= 1
b2 = Bool(bits & 1)
bits >>>= 1
res[pos+b1] = (-1)^b2
pos += 2
end
end
#inbounds for i=1:btail
res[pos+rand(Bool)] = (-1)^rand(Bool)
pos += 2
end
return reinterpret(Complex{Int64},res)
end
#btime test2(1280);
However, the use of reinterpret means to know the bit layout of the different structures. That not a real good idea.
high level view
In the end all what the questioneer has coded was a complicated complex build array of random numbers from 1 to 4 (or 0 to 3). I would try to optimize the next step in questioneers following task. However, no info were supplied.
In that following case julia performs much better, which sounds somewhat strange. More returns, less time??
#btime rand(0:3, 1280)
=> 24.377
PS:
Just for comparing the numbers with Dan Getz last approach, the following code takes 27.004
N=1280
#btime [(-1)^b1*im^b2 for (b1,b2) in zip(rand!(BitVector(N)),rand!(BitVector(N)))]
I have been following the docs for parallel programming in julia and for my mind, which thinks like openMP or MPI, I find the design choice quite strange.
I have an application where I want data to be distributed among processes, and then I want to tell each process to apply some operation to whatever data it is assigned, yet I do not see a way of doing this in Julia. Here is an example
julia> r = remotecall(2, rand, 2)
RemoteRef{Channel{Any}}(2,1,30)
julia> fetch(r)
2-element Array{Float64,1}:
0.733308
0.45227
so on process 2 lives a random array with 2 elements. I can apply some function to this array via
julia> remotecall_fetch(2, getindex, r, 1)
0.7333080770447185
but why does it not work if i apply a function which should change the vector, like:
julia> remotecall_fetch(2, setindex!, r, 1,1)
ERROR: On worker 2:
MethodError: `setindex!` has no method matching setindex!(::RemoteRef{Channel{Any}}, ::Int64, ::Int64)
in anonymous at multi.jl:892
in run_work_thunk at multi.jl:645
[inlined code] from multi.jl:892
in anonymous at task.jl:63
in remotecall_fetch at multi.jl:731
in remotecall_fetch at multi.jl:734
I don't quite know how to describe it, but it seems like the workers can only return "new" things. I don't see how I can send some variables and a function to a worker and have the function modify the variables in place. In the above example, I'd like the array to live on a single process and ideally I'd be able to tell that process to perform some operations on that array. After all the operations are finished I could then fetch results etc.
I think you can achive this with the macro #spawnat:
julia> addprocs(2)
2-element Array{Int64,1}:
2
3
julia> r = remotecall(2, rand, 2)
RemoteRef{Channel{Any}}(2,1,3)
julia> fetch(r)
2-element Array{Float64,1}:
0.149753
0.687653
julia> remotecall_fetch(2, getindex, r, 1)
0.14975250913699378
julia> #spawnat 2 setindex!(fetch(r), 320.0, 1)
RemoteRef{Channel{Any}}(2,1,6)
julia> fetch(r)
2-element Array{Float64,1}:
320.0
0.687653
julia> #spawnat 2 setindex!(fetch(r), 950.0, 2)
RemoteRef{Channel{Any}}(2,1,8)
julia> fetch(r)
2-element Array{Float64,1}:
320.0
950.0
But with remotecall_fetch, it looks like the returned array is really a copy:
julia> remotecall_fetch(2, setindex!, fetch(r), 878.99, 1)
2-element Array{Float64,1}:
878.99
950.0
julia> remotecall_fetch(2, setindex!, fetch(r), 232.99, 2)
2-element Array{Float64,1}:
320.0
232.99
julia> fetch(r)
2-element Array{Float64,1}:
320.0
950.0
with: Julia Version 0.4.3
You may find Distributed Arrays useful, based on the description of your need.