Java Deep Learning Cookbook

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The performance of ND4J operations can also vary upon input array ordering. ND4J enforces the ordering of arrays. Performance in mathematical operations (including general ND4J operations) depends on the input and result array orders. For example, performance in operations such as simple addition, such as z = x + y, will vary in line with the input array orders. It happens due to memory striding: it is easier to read the memory sequence if they're close/adjacent to each other than when they're spread far apart. ND4J is faster on computations with larger matrices. By default, ND4J arrays are C-ordered. IC ordering refers to row-major ordering and the memory allocation resembles that of an array in C:

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