Does ND4J slicing make a copy of the original array?

Question

ND4J INDArray slicing is achieved through one of the overloaded get() methods as answered in java - Get an arbitrary slice of a Nd4j array - Stack Overflow. As an INDArray takes a continuous block of native memory, does slicing using get() make a copy of the original memory (especially row slicing, in which it is possible create a new INDArray with the same backing memory)?

I have found another INDArray method subArray(). Does this one make any difference?

I am asking this because I am trying to create a DatasetIterator that can directly extract data from INDArrays, and I want to eliminate possible overhead. There is too much abstraction in the source code and I couldn't find the implementation myself.

A similar question about NumPy is asked in python - Numpy: views vs copy by slicing - Stack Overflow, and the answer can be found in Indexing — NumPy v1.16 Manual:

The rule of thumb here can be: in the context of lvalue indexing (i.e. the indices are placed in the left hand side value of an assignment), no view or copy of the array is created (because there is no need to). However, with regular values, the above rules for creating views does apply.

Answer 1

The short answer is: no it is using reference when possible. To make a copy the .dup() function can be called.

To quote https://deeplearning4j.org/docs/latest/nd4j-overview

Views: When Two or More NDArrays Refer to the Same Data

A key concept in ND4J is the fact that two NDArrays can actually point to the same underlying data in memory. Usually, we have one NDArray referring to some subset of another array, and this only occurs for certain operations (such as INDArray.get(), INDArray.transpose(), INDArray.getRow() etc. This is a powerful concept, and one that is worth understanding.

There are two primary motivations for this:

There are considerable performance benefits, most notably in avoiding copying arrays We gain a lot of power in terms of how we can perform operations on our NDArrays Consider a simple operation like a matrix transpose on a large (10,000 x 10,000) matrix. Using views, we can perform this matrix transpose in constant time without performing any copies (i.e., O(1) in big O notation), avoiding the considerable cost copying all of the array elements. Of course, sometimes we do want to make a copy - at which point we can use the INDArray.dup() to get a copy. For example, to get a copy of a transposed matrix, use INDArray out = myMatrix.transpose().dup(). After this dup() call, there will be no link between the original array myMatrix and the array out (thus, changes to one will not impact the other).