Software Capabilities of NumPy in Our Tensor Survey Paper
Hi all, I’m a PhD student in Georgia Tech. Recently, we’re working on a survey paper about tensor algorithms: basic tensor operations, tensor decomposition and some tensor applications. We are making a table to compare the capabilities of different software and planning to include NumPy. We’d like to make sure these parameters are correct to make a fair compare. Although we have looked into the related documents, please help us to confirm these. Besides, if you think there are more features of your software and a more preferred citation, please let us know. We’ll consider to update them. We want to show NumPy supports tensors, and we also include "scikit-tensor” in our survey, which is based on NumPy. Please let me know any confusion or any advice! Thanks a lot! :-) Notice: 1. “YES/NO” to show whether or not the software supports the operation or has the feature. 2. “?” means we’re not sure of the feature, and please help us out. 3. “Tensor order” means the maximum number of tensor dimensions that users can do with this software. 4. For computational cores, 1) "Element-wise Tensor Operation (A * B)” includes element-wise add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If the software contains one of them, we mark “YES”. 2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM from tensor contraction. If the software includes tensor contraction, it can also support TTM. 3) For “MTTKRP”, we know most software can realize it through the above two operations. We mark it “YES”, only if an specified optimization for the whole operation. <> <>Software Name <> NumPy Computational Cores Element-wise Tensor Operation (A * B) YES Tensor Contraction (A Xmn B) NO TTM ( A Xn B) NO Matriced Tensor Times Khatri-Rao Product (MTTKRP) NO Tensor Decomposition CP NO Tucker NO Hierarchical Tucker (HT) NO Tensor Train (TT) NO Tensor Features Tensor Order Arbitrary Dense Tensors YES Sparse Tensors NO ? Parallelized NO ? Software Information Application Domain General Programming Environment Python Latest Version 1.10.4 Release Date 2016 Citation: 1. AN DER WALT, S., COLBERT, S., AND VAROQUAUX, G. The NumPy array: A structure for efficient numerical computation. Computing in Science Engineering 13, 2 (March 2011), 22–30. 2. OLIPHANT, T. E. Python for scientific computing. Computing in Science Engineering 9, 3 (May 2007), 10–20. 3. NumPy (Version1.10.4).Available from http://www.numpy.org, Jan <http://www.numpy.org, Jan> 2016. Best regards! Jiajia Li ------------------------------------------ E-mail: jiajiali@gatech.edu Tel: +1 (404)9404603 Computational Science & Engineering Georgia Institute of Technology
Your first citation is incorrect. It is "VAN DER WALT" (missing V in yours) Bryan
On Jan 15, 2016, at 10:36 AM, Li Jiajia <jiajiali@gatech.edu> wrote:
Hi all, I’m a PhD student in Georgia Tech. Recently, we’re working on a survey paper about tensor algorithms: basic tensor operations, tensor decomposition and some tensor applications. We are making a table to compare the capabilities of different software and planning to include NumPy. We’d like to make sure these parameters are correct to make a fair compare. Although we have looked into the related documents, please help us to confirm these. Besides, if you think there are more features of your software and a more preferred citation, please let us know. We’ll consider to update them. We want to show NumPy supports tensors, and we also include "scikit-tensor” in our survey, which is based on NumPy. Please let me know any confusion or any advice! Thanks a lot! :-)
Notice: 1. “YES/NO” to show whether or not the software supports the operation or has the feature. 2. “?” means we’re not sure of the feature, and please help us out. 3. “Tensor order” means the maximum number of tensor dimensions that users can do with this software. 4. For computational cores, 1) "Element-wise Tensor Operation (A * B)” includes element-wise add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If the software contains one of them, we mark “YES”. 2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM from tensor contraction. If the software includes tensor contraction, it can also support TTM. 3) For “MTTKRP”, we know most software can realize it through the above two operations. We mark it “YES”, only if an specified optimization for the whole operation.
Software Name
NumPy
Computational Cores
Element-wise Tensor Operation (A * B)
YES
Tensor Contraction (A Xmn B)
NO
TTM ( A Xn B)
NO
Matriced Tensor Times Khatri-Rao Product (MTTKRP)
NO
Tensor Decomposition
CP
NO
Tucker
NO
Hierarchical Tucker (HT)
NO
Tensor Train (TT)
NO
Tensor Features
Tensor Order
Arbitrary
Dense Tensors
YES
Sparse Tensors
NO ?
Parallelized
NO ?
Software Information
Application Domain
General
Programming Environment
Python
Latest Version
1.10.4 Release Date
2016
Citation: 1. AN DER WALT, S., COLBERT, S., AND VAROQUAUX, G. The NumPy array: A structure for efficient numerical computation. Computing in Science Engineering 13, 2 (March 2011), 22–30. 2. OLIPHANT, T. E. Python for scientific computing. Computing in Science Engineering 9, 3 (May 2007), 10–20. 3. NumPy (Version1.10.4).Available from http://www.numpy.org, Jan 2016.
Best regards! Jiajia Li
------------------------------------------ E-mail: jiajiali@gatech.edu Tel: +1 (404)9404603 Computational Science & Engineering Georgia Institute of Technology
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
On Jan 15, 2016 8:36 AM, "Li Jiajia" <jiajiali@gatech.edu> wrote:
Hi all, I’m a PhD student in Georgia Tech. Recently, we’re working on a survey
Please let me know any confusion or any advice! Thanks a lot! :-)
Notice: 1. “YES/NO” to show whether or not the software supports the operation or has the feature. 2. “?” means we’re not sure of the feature, and please help us out. 3. “Tensor order” means the maximum number of tensor dimensions that users can do with this software. 4. For computational cores, 1) "Element-wise Tensor Operation (A * B)” includes element-wise add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If
paper about tensor algorithms: basic tensor operations, tensor decomposition and some tensor applications. We are making a table to compare the capabilities of different software and planning to include NumPy. We’d like to make sure these parameters are correct to make a fair compare. Although we have looked into the related documents, please help us to confirm these. Besides, if you think there are more features of your software and a more preferred citation, please let us know. We’ll consider to update them. We want to show NumPy supports tensors, and we also include "scikit-tensor” in our survey, which is based on NumPy. the software contains one of them, we mark “YES”.
2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM from tensor contraction. If the software includes tensor contraction, it can also support TTM. 3) For “MTTKRP”, we know most software can realize it through the above two operations. We mark it “YES”, only if an specified optimization for the whole operation.
NumPy has support for working with multidimensional tensors, if you like, but it doesn't really use the tensor language and notation (preferring instead to think in terms of "arrays" as a somewhat more computationally focused and less mathematically focused conceptual framework). Which is to say that I actually have no idea what all those jargon terms you're asking about mean :-) I am suspicious that NumPy supports more of those operations than you have marked, just under different names/notation, but really can't tell either way for sure without knowing what exactly they are. (It is definitely correct though that NumPy includes no support for sparse tensors, and NumPy itself is not multi-threaded beyond what we get for free through the BLAS, though there are external libraries that can perform multi-threaded computations on top of data stored in numpy arrays.) -n
On Fri, Jan 15, 2016 at 5:30 PM, Nathaniel Smith <njs@pobox.com> wrote:
On Jan 15, 2016 8:36 AM, "Li Jiajia" <jiajiali@gatech.edu> wrote:
Hi all, I’m a PhD student in Georgia Tech. Recently, we’re working on a survey
Please let me know any confusion or any advice! Thanks a lot! :-)
Notice: 1. “YES/NO” to show whether or not the software supports the operation or has the feature. 2. “?” means we’re not sure of the feature, and please help us out. 3. “Tensor order” means the maximum number of tensor dimensions that users can do with this software. 4. For computational cores, 1) "Element-wise Tensor Operation (A * B)” includes element-wise add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If
2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM from tensor contraction. If the software includes tensor contraction, it can also support TTM. 3) For “MTTKRP”, we know most software can realize it through the above two operations. We mark it “YES”, only if an specified optimization for the whole operation.
NumPy has support for working with multidimensional tensors, if you like, but it doesn't really use the tensor language and notation (preferring instead to think in terms of "arrays" as a somewhat more computationally focused and less mathematically focused conceptual framework).
Which is to say that I actually have no idea what all those jargon terms you're asking about mean :-) I am suspicious that NumPy supports more of
paper about tensor algorithms: basic tensor operations, tensor decomposition and some tensor applications. We are making a table to compare the capabilities of different software and planning to include NumPy. We’d like to make sure these parameters are correct to make a fair compare. Although we have looked into the related documents, please help us to confirm these. Besides, if you think there are more features of your software and a more preferred citation, please let us know. We’ll consider to update them. We want to show NumPy supports tensors, and we also include "scikit-tensor” in our survey, which is based on NumPy. the software contains one of them, we mark “YES”. those operations than you have marked, just under different names/notation, but really can't tell either way for sure without knowing what exactly they are. In particular check if your operations can be expressed with einsum() http://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.einsum.html -- Robert Kern
I echo with Robert that the contraction can be done with np.einsum(). Also, check out the np.tensordot() as well - it can also be used to perform contraction. Shawn On Fri, Jan 15, 2016 at 12:32 PM, Robert Kern <robert.kern@gmail.com> wrote:
On Fri, Jan 15, 2016 at 5:30 PM, Nathaniel Smith <njs@pobox.com> wrote:
On Jan 15, 2016 8:36 AM, "Li Jiajia" <jiajiali@gatech.edu> wrote:
Hi all, I’m a PhD student in Georgia Tech. Recently, we’re working on a survey paper about tensor algorithms: basic tensor operations, tensor decomposition and some tensor applications. We are making a table to compare the capabilities of different software and planning to include NumPy. We’d like to make sure these parameters are correct to make a fair compare. Although we have looked into the related documents, please help us to confirm these. Besides, if you think there are more features of your software and a more preferred citation, please let us know. We’ll consider to update them. We want to show NumPy supports tensors, and we also include "scikit-tensor” in our survey, which is based on NumPy. Please let me know any confusion or any advice! Thanks a lot! :-)
Notice: 1. “YES/NO” to show whether or not the software supports the operation or has the feature. 2. “?” means we’re not sure of the feature, and please help us out. 3. “Tensor order” means the maximum number of tensor dimensions that users can do with this software. 4. For computational cores, 1) "Element-wise Tensor Operation (A * B)” includes element-wise add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If the software contains one of them, we mark “YES”. 2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM from tensor contraction. If the software includes tensor contraction, it can also support TTM. 3) For “MTTKRP”, we know most software can realize it through the above two operations. We mark it “YES”, only if an specified optimization for the whole operation.
NumPy has support for working with multidimensional tensors, if you like, but it doesn't really use the tensor language and notation (preferring instead to think in terms of "arrays" as a somewhat more computationally focused and less mathematically focused conceptual framework).
Which is to say that I actually have no idea what all those jargon terms you're asking about mean :-) I am suspicious that NumPy supports more of those operations than you have marked, just under different names/notation, but really can't tell either way for sure without knowing what exactly they are.
In particular check if your operations can be expressed with einsum()
http://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.einsum.html
-- Robert Kern
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
-- Yuxiang "Shawn" Wang Gerling Haptics Lab University of Virginia yw5aj@virginia.edu +1 (434) 284-0836 https://sites.google.com/a/virginia.edu/yw5aj/
Robert beat me to it on einsum, but also check tensordot for general tensor contraction. On Fri, Jan 15, 2016 at 9:30 AM, Nathaniel Smith <njs@pobox.com> wrote:
On Jan 15, 2016 8:36 AM, "Li Jiajia" <jiajiali@gatech.edu> wrote:
Hi all, I’m a PhD student in Georgia Tech. Recently, we’re working on a survey
Please let me know any confusion or any advice! Thanks a lot! :-)
Notice: 1. “YES/NO” to show whether or not the software supports the operation or has the feature. 2. “?” means we’re not sure of the feature, and please help us out. 3. “Tensor order” means the maximum number of tensor dimensions that users can do with this software. 4. For computational cores, 1) "Element-wise Tensor Operation (A * B)” includes element-wise add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If
2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM from tensor contraction. If the software includes tensor contraction, it can also support TTM. 3) For “MTTKRP”, we know most software can realize it through the above two operations. We mark it “YES”, only if an specified optimization for the whole operation. NumPy has support for working with multidimensional tensors, if you like, but it doesn't really use the tensor language and notation (preferring instead to think in terms of "arrays" as a somewhat more computationally focused and less mathematically focused conceptual framework). Which is to say that I actually have no idea what all those jargon terms you're asking about mean :-) I am suspicious that NumPy supports more of
paper about tensor algorithms: basic tensor operations, tensor decomposition and some tensor applications. We are making a table to compare the capabilities of different software and planning to include NumPy. We’d like to make sure these parameters are correct to make a fair compare. Although we have looked into the related documents, please help us to confirm these. Besides, if you think there are more features of your software and a more preferred citation, please let us know. We’ll consider to update them. We want to show NumPy supports tensors, and we also include "scikit-tensor” in our survey, which is based on NumPy. the software contains one of them, we mark “YES”. those operations than you have marked, just under different names/notation, but really can't tell either way for sure without knowing what exactly they are. (It is definitely correct though that NumPy includes no support for sparse tensors, and NumPy itself is not multi-threaded beyond what we get for free through the BLAS, though there are external libraries that can perform multi-threaded computations on top of data stored in numpy arrays.) -n
participants (6)
-
Bryan Van de Ven -
Li Jiajia -
Nathaniel Smith -
Robert Kern -
Stephan Hoyer -
Yuxiang Wang