[Numpy-discussion] numpy allocation event hooks
Thouis (Ray) Jones
Mon Jun 18 05:14:34 CDT 2012
Based on some previous discussion on the numpy list  and in
now-cancelled PRs [2,3], I'd like to solicit opinions on adding an
interface for numpy memory allocation event tracking, as implemented
in this PR:
A brief summary of the changes:
- PyDataMem_NEW/FREE/RENEW become functions in the numpy API.
(they used to be macros for malloc/free/realloc)
These are the functions used to manage allocations for array's
internal data. Most other numpy data is allocated through Python's
- PyDataMem_NEW/RENEW return void* instead of char*.
- Adds PyDataMem_SetEventHook() to the API, with this description:
* Sets the allocation event hook for numpy array data.
* Takes a PyDataMem_EventHookFunc *, which has the signature:
* void hook(void *old, void *new, size_t size, void *user_data).
* Also takes a void *user_data, and void **old_data.
* Returns a pointer to the previous hook or NULL. If old_data is
* non-NULL, the previous user_data pointer will be copied to it.
* If not NULL, hook will be called at the end of each PyDataMem_NEW/FREE/RENEW:
* result = PyDataMem_NEW(size) -> (*hook)(NULL, result,
* PyDataMem_FREE(ptr) -> (*hook)(ptr, NULL, 0, user_data)
* result = PyDataMem_RENEW(ptr, size) -> (*hook)(ptr, result, size,
* When the hook is called, the GIL will be held by the calling
* thread. The hook should be written to be reentrant, if it performs
* operations that might cause new allocation events (such as the
* creation/descruction numpy objects, or creating/destroying Python
* objects which might cause a gc)
The PR also includes an example using the hook functions to track
allocation via Python callback funcions (in
Why I think this is worth adding to numpy, even though other tools may
be able to provide similar functionality:
- numpy arrays use orders of magnitude more memory than most python
objects, and this is often a limiting factor in algorithms.
- numpy can behave in complicated ways with regards to memory
management, e.g., views, OWNDATA, temporaries, etc., making it
sometimes difficult to know where memory usage problems are
happening and why.
- numpy attracts a large number of programmers with limited low-level
programming expertise, and who don't have the skills to use external
tools (or time/motivation to acquire those skills), but still need
to be able to diagnose these sorts of problems.
- Other tools are not well integrated with Python, and vary a great
deal between OS and compiler setup.
I appreciate any feedback.
 (python callbacks) https://github.com/numpy/numpy/pull/284
 (C-level logging) https://github.com/numpy/numpy/pull/301
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