[ruby-core:100144] [Ruby master Feature#13767] add something like python's buffer protocol to share memory between different narray like classes

Issue #13767 has been updated by mrkn (Kenta Murata).

Status changed from Open to Closed

Fixed by https://github.com/ruby/ruby/commit/890bc2cdde4097390f3b71dfeaa36dd92ee0afe2

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Feature #13767: add something like python's buffer protocol to share memory between different narray like classes
https://bugs.ruby-lang.org/issues/13767#change-87718

* Author: dsisnero (Dominic Sisneros)
* Status: Closed
* Priority: Normal
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In order for ruby to be used in more scientific or machine learning applications, it will be necessary to be able to use more memory efficient data structures.  Python has a concept called Buffer Protocol that allows different representations to utilize the memory without copying the date with memory views.  This is a proposal to add something similar to ruby as a c-api 

https://jakevdp.github.io/blog/2014/05/05/introduction-to-the-python-buffer-protocol/

The Python buffer protocol, also known in the community as PEP 3118, is a framework in which Python objects can expose raw byte arrays to other Python objects. This can be extremely useful for scientific computing, where we often use packages such as NumPy to efficiently store and manipulate large arrays of data. Using the buffer protocol, we can let multiple objects efficiently manipulate views of the same data buffers, without having to make copies of the often large datasets.

Here, for example, we'll use Python's built-in array object to create an array:

In [1]:
import array
A = array.array('i', range(10))
A
Out[1]:
array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Note that the array object is different than a Python list, in that it stores the data as a contiguous block of integers. For this reason, the data are stored much more compactly than a list, and certain operations can be performed much more quickly.

array objects by themselves are not particularly useful, but a similar type of object can be found in the numpy array. Because both Python's array and NumPy's ndarray objects implement the buffer protocol, it's possible to seamlessly pass data between them using views – that is, without the need to copy the raw data:



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