numpy矩阵乘法或einsum运算中的MemoryError

Question

I am working with binary (only 0's and 1's) matrices of rows and columns in the order of a few thousands. For example, the number of rows are between 2000 - 7000 and number of columns are between 4000 - 15000. My computer has more then 100g RAM.

I'm surprised that even with these sizes, I am getting MemoryError with the following code. For reproducibility, I'm including an example with a smaller matrix (10*20) Note than both of the following raise this error:

   import numpy as np

   my_matrix = np.random.randint(2,size=(10,20))
   tr, tc = np.triu_indices(my_matrix.shape[0],1)
   ut_sums = np.sum(my_matrix[tr] * my_matrix[tc], 1)

   denominator = 100
   value = 1 - ut_sums.astype(float)/denominator
   np.einsum('i->', value)

I tried to replace the elementwise multiplication in the above code to einsum as below, but it also generates the same MemoryError:

   import numpy as np

   my_matrix = np.random.randint(2,size=(10,20))
   tr, tc = np.triu_indices(my_matrix.shape[0],1)
   ut_sums = np.einsum('ij,ij->i', my_matrix[tr], my_matrix[tc])

   denominator = 100
   value = 1 - ut_sums.astype(float)/denominator
   np.einsum('i->', value)

In both cases, the printed Traceback points to the line where ut_sums is being calculated.

Please note that my code has other operations too, and there are other statistics calculated on matrices of similar sizes, but with more than 100 g, I thought it should not be a problem.

Answer 1

Just because your computer has 100 GB of physical memory does not mean that your operating system is willing or able to allocate such large amounts of contiguous memory. And it does have to be contiguous, because that's how NumPy arrays usually are.

You should figure out how large your output matrix is meant to be, and then try creating a similar one by itself:

arr = np.zeros((10000, 10000))

See if you're able to allocate a single array as large as you want.