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Performance: Difference between revisions
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=== Alternate array representations === | === Alternate array representations === | ||
Internally, APL arrays are usually stored as two lists in memory. The first is a list of the shape (although it's also possible to store the "stride", enabling different views of the same data<ref>NumPy Reference. [https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html "ndarray.strides"]. Accessed 2020-11-09.</ref><ref> | Internally, APL arrays are usually stored as two lists in memory. The first is a list of the shape (although it's also possible to store the "stride", enabling different views of the same data<ref>NumPy Reference. [https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html "ndarray.strides"]. Accessed 2020-11-09.</ref><ref>Nick Nickolov. [http://archive.vector.org.uk/art10501160 "Compiling APL to JavaScript"]. [[Vector Journal]] Volume 26 No. 1. 2013-09. (The strided representation was later removed from [[ngn/apl]].)</ref>). The second is the ravel of elements in the array. Nested arrays consist of pointers to arrays which may be distributed across memory, their use can lead to very inefficient memory read patterns - in contrast to flat arrays which are stored as a contiguous block. | ||
=== Reference counting and data reuse === | === Reference counting and data reuse === | ||