GPU Array Computing¶
- Authors:
Emily Costa
- Created on:
08/07/2019
The following are lessons learned during the exploration of implementing CuPy instead of numpy for GPU computing:
newaxis¶
Dimensions: CuPy does not have a newaxis
function unlike NumPy.
Instead of using new axis to add an additional dimension, you need to use cupy.expand_dims()
.
Also, note that CuPy does not lose a dimension during operations with vectors unlike numpy.
So, adding another dimension is often unnecessary as there are no singular dimensions in CuPy.
All vectors are converted into row vectors in numpy after being operated on,
which can be dealt with by adding a new axis and converting back into a column vector for further matrix operations.
The following is an example of how numpy’s neawaxis function and how to use cupy’s expand_dims in its place:
numpy.newaxis¶
Import all necessary modules
In [1]: import numpy as np
1D array:
In [2]: arr = np.arange(5)
In [3]: arr.shape
Out[3]: (5,)
Make the 1D array becomes a row vector when an axis is inserted along 1st dimension
In [4]: row_vec = arr[np.newaxis, :]
In [5]: row_vec.shape
Out[5]: (1, 5)
Make the 1D array becomes a column vector when an axis is inserted along 1st dimension
In [6]: col_vec = arr[:, np.newaxis]
In [7]: col_vec.shape
Out[7]: (5, 1)
cupy.expand_dims¶
Import all necessary modules
In [1]: import cupy as cp
1D array
In [2]: cp_arr = cp.arange(5)
In [3]: cp_arr.shape
Out[3]: (5,)
Make the 1D array becomes a row vector when an axis is inserted along 1st dimension
In [4]: cp_row_vec = cp.expand_dims(cp_arr, axis=0)
In [5]: cp_row_vec.shape
Out[5]: (1, 5)
Make the 1D array becomes a column vector when an axis is inserted along 1st dimension
In [6]: cp_col_vec = cp.expand_dims(cp_arr, axis=1)
In [7]: cp_col_vec.shape
Out[7]: (5, 1)
append¶
CuPy does not have an append()
function unlike NumPy.
As a reminder - the append
function in the NumPy appends values to the end of an array.
The following is an example of numpy’s append
function and how to use cupy’s concatenate
instead:
numpy.append¶
In [1]: x = np.array([1,2,3])
In [2]: y = [4,5,6]
In [3]: xy = np.append(x, y)
In [4]: xy
Out[4]: array([1,2,3,4,5,6])
cupy.concatenate¶
In [1]: cp_x = cp.array([1,2,3])
In [2]: cp_y = cp.array([4,5,6])
In [3]: cp_xy = cp.concatenate([cp_x,cp_y], axis=0)
In [4]: cp_xy
Out[4]: [1 2 3 4 5 6]