How to implement numpy broadcast mechanism with mkl?
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How to implement numpy broadcast mechanism with mkl?
I have been confused, how to use mkl to efficiently implement the broadcast mechanism in numpy (Element wise operator "+","-","*")?
such as
2-D array sub 1-D array
[[1,2,3], [[0,0,0],
[4,5,6], - [1,2,3] = [3,3,3],
[7,8,9]] [6,6,6]]
And the second operation (can be understood as a matrix multiplied by a diagonal matrix)
2-D array multiply 1-D array(Element wise multiply )
[[1,2,3], [[1,4,9],
[4,5,6], * [1,2,3] = [4,10,18],
[7,8,9]] [7,16,27]]
I tried to implement with the for loop +cblas_dscal/vdSub.
But I think this is not efficient, I don't know if there is any better implementation.
intel-mkl
asked Jan 4 at 14:39
梁先锋梁先锋
1
add a comment |
How to implement numpy broadcast mechanism with mkl?
I have been confused, how to use mkl to efficiently implement the broadcast mechanism in numpy (Element wise operator "+","-","*")?
such as
2-D array sub 1-D array
[[1,2,3], [[0,0,0],
[4,5,6], - [1,2,3] = [3,3,3],
[7,8,9]] [6,6,6]]
And the second operation (can be understood as a matrix multiplied by a diagonal matrix)
2-D array multiply 1-D array(Element wise multiply )
[[1,2,3], [[1,4,9],
[4,5,6], * [1,2,3] = [4,10,18],
[7,8,9]] [7,16,27]]
I tried to implement with the for loop +cblas_dscal/vdSub.
But I think this is not efficient, I don't know if there is any better implementation.
intel-mkl
asked Jan 4 at 14:39
梁先锋梁先锋
1
Themklcode is called by compiled numpy code. You don't invoke it directly from Python. It's a library that is linked tonumpyduring setup or compilation, not something you consciously use while running interpreted code.
– hpaulj
Jan 4 at 21:24
Sorry, I didn’t say it clearly. I am using c++ and mkl now, not python...
– 梁先锋
Jan 5 at 12:24
add a comment |
How to implement numpy broadcast mechanism with mkl?
I have been confused, how to use mkl to efficiently implement the broadcast mechanism in numpy (Element wise operator "+","-","*")?
such as
2-D array sub 1-D array
[[1,2,3], [[0,0,0],
[4,5,6], - [1,2,3] = [3,3,3],
[7,8,9]] [6,6,6]]
And the second operation (can be understood as a matrix multiplied by a diagonal matrix)
2-D array multiply 1-D array(Element wise multiply )
[[1,2,3], [[1,4,9],
[4,5,6], * [1,2,3] = [4,10,18],
[7,8,9]] [7,16,27]]
I tried to implement with the for loop +cblas_dscal/vdSub.
But I think this is not efficient, I don't know if there is any better implementation.
intel-mkl
asked Jan 4 at 14:39
梁先锋梁先锋
1
How to implement numpy broadcast mechanism with mkl?
I have been confused, how to use mkl to efficiently implement the broadcast mechanism in numpy (Element wise operator "+","-","*")?
such as
2-D array sub 1-D array
[[1,2,3], [[0,0,0],
[4,5,6], - [1,2,3] = [3,3,3],
[7,8,9]] [6,6,6]]
And the second operation (can be understood as a matrix multiplied by a diagonal matrix)
2-D array multiply 1-D array(Element wise multiply )
[[1,2,3], [[1,4,9],
[4,5,6], * [1,2,3] = [4,10,18],
[7,8,9]] [7,16,27]]
I tried to implement with the for loop +cblas_dscal/vdSub.
But I think this is not efficient, I don't know if there is any better implementation.
intel-mkl
intel-mkl
asked Jan 4 at 14:39
梁先锋梁先锋
1
asked Jan 4 at 14:39
梁先锋梁先锋
1
edited Jan 5 at 12:22
梁先锋
asked Jan 4 at 14:39
梁先锋梁先锋
1
asked Jan 4 at 14:39
梁先锋梁先锋
1
asked Jan 4 at 14:39
梁先锋梁先锋
1
1
Themklcode is called by compiled numpy code. You don't invoke it directly from Python. It's a library that is linked tonumpyduring setup or compilation, not something you consciously use while running interpreted code.
– hpaulj
Jan 4 at 21:24
Sorry, I didn’t say it clearly. I am using c++ and mkl now, not python...
– 梁先锋
Jan 5 at 12:24
add a comment |
Themklcode is called by compiled numpy code. You don't invoke it directly from Python. It's a library that is linked tonumpyduring setup or compilation, not something you consciously use while running interpreted code.
– hpaulj
Jan 4 at 21:24
Sorry, I didn’t say it clearly. I am using c++ and mkl now, not python...
– 梁先锋
Jan 5 at 12:24
The
mkl code is called by compiled numpy code. You don't invoke it directly from Python. It's a library that is linked to numpy during setup or compilation, not something you consciously use while running interpreted code.– hpaulj
Jan 4 at 21:24
The
mkl code is called by compiled numpy code. You don't invoke it directly from Python. It's a library that is linked to numpy during setup or compilation, not something you consciously use while running interpreted code.– hpaulj
Jan 4 at 21:24
Sorry, I didn’t say it clearly. I am using c++ and mkl now, not python...
– 梁先锋
Jan 5 at 12:24
Sorry, I didn’t say it clearly. I am using c++ and mkl now, not python...
– 梁先锋
Jan 5 at 12:24
add a comment |
1 Answer
1
active
oldest
votes
You can just broadcast the second array to two dimensions with arr2[None, :]. This yields the following code:
arr1 = np.array([[1,2,3],
[4,5,6],
[7,8,9]])
arr2 = np.array([1,2,3])
print(arr1 - arr2[None, :])
# Out: [[0 0 0]
# [3 3 3]
# [6 6 6]]
print(arr1 * arr2[None, :])
# Out: [[ 1, 4, 9],
# [ 4, 10, 18],
# [ 7, 16, 27]]
If you broadcast your arrays like this, numpy will use optimizations like mkl to perform the requested operations like multiplications.
More information on broadcasting and expanding array dimensions with None or np.newaxis can be found here:
- Broadcasting
- Expancing dimensions
answered Jan 4 at 14:56
Scotty1-Scotty1-
2,2551424
Thanks. but I am using c++ and mkl now, not python
– 梁先锋
Jan 5 at 12:24
You may still upvote and accept my answer, since it answered your question. :)
– Scotty1-
Jan 5 at 17:21
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
You can just broadcast the second array to two dimensions with arr2[None, :]. This yields the following code:
arr1 = np.array([[1,2,3],
[4,5,6],
[7,8,9]])
arr2 = np.array([1,2,3])
print(arr1 - arr2[None, :])
# Out: [[0 0 0]
# [3 3 3]
# [6 6 6]]
print(arr1 * arr2[None, :])
# Out: [[ 1, 4, 9],
# [ 4, 10, 18],
# [ 7, 16, 27]]
If you broadcast your arrays like this, numpy will use optimizations like mkl to perform the requested operations like multiplications.
More information on broadcasting and expanding array dimensions with None or np.newaxis can be found here:
- Broadcasting
- Expancing dimensions
answered Jan 4 at 14:56
Scotty1-Scotty1-
2,2551424
Thanks. but I am using c++ and mkl now, not python
– 梁先锋
Jan 5 at 12:24
You may still upvote and accept my answer, since it answered your question. :)
– Scotty1-
Jan 5 at 17:21
add a comment |
You can just broadcast the second array to two dimensions with arr2[None, :]. This yields the following code:
arr1 = np.array([[1,2,3],
[4,5,6],
[7,8,9]])
arr2 = np.array([1,2,3])
print(arr1 - arr2[None, :])
# Out: [[0 0 0]
# [3 3 3]
# [6 6 6]]
print(arr1 * arr2[None, :])
# Out: [[ 1, 4, 9],
# [ 4, 10, 18],
# [ 7, 16, 27]]
If you broadcast your arrays like this, numpy will use optimizations like mkl to perform the requested operations like multiplications.
More information on broadcasting and expanding array dimensions with None or np.newaxis can be found here:
- Broadcasting
- Expancing dimensions
answered Jan 4 at 14:56
Scotty1-Scotty1-
2,2551424
Thanks. but I am using c++ and mkl now, not python
– 梁先锋
Jan 5 at 12:24
You may still upvote and accept my answer, since it answered your question. :)
– Scotty1-
Jan 5 at 17:21
add a comment |
You can just broadcast the second array to two dimensions with arr2[None, :]. This yields the following code:
arr1 = np.array([[1,2,3],
[4,5,6],
[7,8,9]])
arr2 = np.array([1,2,3])
print(arr1 - arr2[None, :])
# Out: [[0 0 0]
# [3 3 3]
# [6 6 6]]
print(arr1 * arr2[None, :])
# Out: [[ 1, 4, 9],
# [ 4, 10, 18],
# [ 7, 16, 27]]
If you broadcast your arrays like this, numpy will use optimizations like mkl to perform the requested operations like multiplications.
More information on broadcasting and expanding array dimensions with None or np.newaxis can be found here:
- Broadcasting
- Expancing dimensions
answered Jan 4 at 14:56
Scotty1-Scotty1-
2,2551424
You can just broadcast the second array to two dimensions with arr2[None, :]. This yields the following code:
arr1 = np.array([[1,2,3],
[4,5,6],
[7,8,9]])
arr2 = np.array([1,2,3])
print(arr1 - arr2[None, :])
# Out: [[0 0 0]
# [3 3 3]
# [6 6 6]]
print(arr1 * arr2[None, :])
# Out: [[ 1, 4, 9],
# [ 4, 10, 18],
# [ 7, 16, 27]]
If you broadcast your arrays like this, numpy will use optimizations like mkl to perform the requested operations like multiplications.
More information on broadcasting and expanding array dimensions with None or np.newaxis can be found here:
- Broadcasting
- Expancing dimensions
answered Jan 4 at 14:56
Scotty1-Scotty1-
2,2551424
edited Jan 4 at 15:01
answered Jan 4 at 14:56
Scotty1-Scotty1-
2,2551424
answered Jan 4 at 14:56
Scotty1-Scotty1-
2,2551424
answered Jan 4 at 14:56
Scotty1-Scotty1-
2,2551424
2,2551424
Thanks. but I am using c++ and mkl now, not python
– 梁先锋
Jan 5 at 12:24
You may still upvote and accept my answer, since it answered your question. :)
– Scotty1-
Jan 5 at 17:21
add a comment |
Thanks. but I am using c++ and mkl now, not python
– 梁先锋
Jan 5 at 12:24
You may still upvote and accept my answer, since it answered your question. :)
– Scotty1-
Jan 5 at 17:21
Thanks. but I am using c++ and mkl now, not python
– 梁先锋
Jan 5 at 12:24
Thanks. but I am using c++ and mkl now, not python
– 梁先锋
Jan 5 at 12:24
You may still upvote and accept my answer, since it answered your question. :)
– Scotty1-
Jan 5 at 17:21
You may still upvote and accept my answer, since it answered your question. :)
– Scotty1-
Jan 5 at 17:21
add a comment |
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The
mklcode is called by compiled numpy code. You don't invoke it directly from Python. It's a library that is linked tonumpyduring setup or compilation, not something you consciously use while running interpreted code.– hpaulj
Jan 4 at 21:24
Sorry, I didn’t say it clearly. I am using c++ and mkl now, not python...
– 梁先锋
Jan 5 at 12:24