[Numpy-discussion] numpy allocation event hooks
Thouis (Ray) Jones
Mon Jun 18 08:58:19 CDT 2012
On Mon, Jun 18, 2012 at 3:46 PM, Dag Sverre Seljebotn
> On 06/18/2012 12:14 PM, Thouis (Ray) Jones wrote:
>> 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,
>> size, user_data)
>> * 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.
> Are the hooks able to change how allocation happens/override allocation?
> If one goes to this much pain already, I think one might as well go the
> extra step and allow hooks to override memory allocation.
> At least something to think about -- of course the above (as I
> understand it) would be a good start on a pluggable allocator even if it
> isn't done right away.
> - Allocate NumPy arrays in process-shared memory using shmem/mmap
> - Allocate NumPy arrays on some boundary (16-byte, 4096-byte..) using
That's not present in the current change, but the choice to use
"EventHook" rather than the more generic "Hook" was to avoid colliding
with a change like that in the future.
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