Memory Growth With Broadcast Operations In Numpy

I am using NumPy to handle some large data matrices (of around ~50GB in size). The machine where I am running this code has 128GB of RAM so doing simple linear operations of this m

Solution 1:

@rth's suggestion to do the operation in smaller batches is a good one. You could also try using the function np.subtract and give it the destination array to avoid creating an addtional temporary array. I also think you don't need to index c as c[np.newaxis, :, :], because it is already a 3-d array.

So instead of

a[:]= b[:,:, np.newaxis]-c[np.newaxis,:,:]# memory explodes here

try

np.subtract(b[:, :, np.newaxis], c, a)

The third argument of np.subtract is the destination array.

Solution 2:

Well, your array a takes already 1192953*192*32* 8 bytes/1.e9 = 58 GB of memory.

The broadcasting does not make additional memory allocations for the initial arrays, but the result of

b[:, :, np.newaxis] - c[np.newaxis, :, :]

is still saved in a temporary array. Therefore at this line, you have allocated at least 2 arrays with the shape of a for a total memory used >116 GB.

You can avoid this issue, by operating on a smaller subset of your array at one time,

CHUNK_SIZE = 100000for idx inrange(b.shape[0]/CHUNK_SIZE):
    sl = slice(idx*CHUNK_SIZE, (idx+1)*CHUNK_SIZE)
    a[sl] = b[sl, :, np.newaxis] - c[np.newaxis, :, :]

this will be marginally slower, but uses much less memory.