I was trying to use broadcast! in place of broadcast in the following code :
function _tensor_product_(st)
ten = st[1]
for s in st[2:end]
ten = kron(ten, s)
end
return ten
end
@noinline function indi(arr, ind1, ind2, no_of_e)
linit = [speye(2) for e=1:no_of_e]
op = spzeros(2^no_of_e, 2^no_of_e)
op1 = spzeros(2^no_of_e, 2^no_of_e)
for elem in arr[ind1:ind2]
println(" elem ", elem)
for e in elem
linit[e] = SparseMatrixCSC([0. 1.;1. 0.])
end
tp = _tensor_product_(linit)
op += tp
# op1 = broadcast(+, op1, tp)
broadcast!(+, op1, op1, tp)
# resetting the configuration for next iteration
for i=1:length(linit)
linit[i] = speye(2)
end
println(op1 == op)
gc()
end
return op
end
r = [[1, 3, 6, 4], [2, 4, 7, 5], [6, 8, 1, 9], [7, 9, 2, 10]]
indi(r, 1, 4, 14)
The behavior of broadcast and broadcast! are not the same, while the former works as I expect but the latter does not replicate the former (was trying to do it in place).
Also, if I have the following the memory shoots up, one way to tackle is to use mmap and store it to disk as a tensor product is being performed. So how to use mmap in conjugation with sparse matrices and also are there any general methods to optimize the above routine for memory efficiency ?
# a = [[1, 3, 6, 4], [2, 4, 7, 5], [6, 8, 11, 9], [7, 9, 12, 10], [11, 13, 16, 14], [12, 14, 17, 15], [16, 18, 1, 19], [17, 19, 2, 20]]
# indi(a, 1, 4, 28)
Thanks !