We can drop the parameter if the allocator has always the size alignment, but each object may require a different aligment? Whether we use them in Python core is another discussion.
Nathaniel, what APIs would you need exactly? See Victor's proposal in msg Allocating more memory and skip first bytes may work, but how do we retrieve the original address if the function releasing the memory block? Are there such platforms? OS functions don't require the GIL. This kind of aligned allocation is effectively its own memory domain.
If native aligned allocation support were added to PyMalloc then it could potentially do better e. This might be the ideal solution for Raymond's use case, but I have no idea how much work it would be to mess around with PyMalloc innards. Numpy doesn't currently use aligned allocation for anything, but we'd like to keep our options open.
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OTOH we are also happy to implement the aligned allocation ourselves either on top of the system APIs or directly -- we just don't want to lose tracemalloc support when we do. For numpy's purposes, I think the best approach would be to add a tracemalloc "escape valve", with an interface like: This would be useful in a number of cases: Potentially even open file descriptors if one wants to go there seems pretty useful, actually.
How about a new domain instead? Currently all the domains share the same vtable struct, though, whereas aligned allocator functions have different signatures. So you can't literally just add an entry to the existing domain enum and be done. I guess this is another argument that it might be best to just give numpy an escape valve and worry about CPython's internal needs separately. You have to add new members to the structure or add a new structure. Also, Numpy wants calloc for faster allocation of zeroed arrays Since Python 3.
[libdispatch-dev] Solaris portability, POSIX memalign checking in configure.ac - PATCH [1/6]
Moreover, I also added second feature to tracemalloc: See the issue With these new tracemalloc features, I don't think that NumPy can still be used to request this feature in CPython core. See also the change 6e16bcf1 , it seems like Raymond doesn't plan to use this feature anymore.
I would like to set this https: I think many people would welcome this in scientific computing: The Arrow memory format for example recommends 64 bit alignment. I want to set But the other part is that datashape allows to specify alignment regardless of the size of the type. There are several other cases in datashape where alignment can specified explicitly.
These functions were made public in Python 3.
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I made this change to allow numpy to trace memory allocations, to debug memory leaks. Stefan Krah: Ah, that's an interesting use case. Raymond Hettinger: Statistics size on my PR: Ah, too bad. Of course Windows does something different again. This weakens my use case somewhat, but I guess it would still be nice to have the functions if you think it's maintainable. I presume you mean 64 bytes? Yes, I was typing too fast. Benjamin Peterson benjamin. See though, we only need byte alignment there msg - view Author: So I retract the weakening of my use case still shame on Microsoft for not implementing C Or can they leave those pointers NULL and get a default implementation?
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Technically, we can implement a fallback, but I'm not sure that I want to do that: But is Python suppose to fills the holes? In Python 3. In case of doubt, I prefer not to guess, and leave the decision to the caller of the API: I'm not sure that it's a good idea to provide a "aligned malloc" fallback if such fallback would be inefficient. An aligned memory block would look like: According to Stackoverflow MinGW has an internal function. Android, I don't know. You can re-use the same bytes for padding and to store the offset. The main tricky thing is that for an alignment of N bytes you need to overallocate N bytes instead of N In the worst case, malloc returns you a pointer that's already N-byte aligned, and then you have to advance it by a full N bytes so that you have some space before the pointer to store the offset.
And make sure that N is a power-of-two, which guarantees that whatever value malloc returns will be shifted by at least malloc's regular alignment, which is guaranteed to be large enough to store a standard int type on reasonable systems. On the other hand, sane requests will have the exact multiple most of the time anyway.
The ways we've discussed using aligned allocation in numpy wouldn't follow this requirement without special checking. Which isn't necessarily a big deal, and numpy won't necessarily use this API anyway. But I would suggest being very clear about exactly what you guarantee and what you don't: In the GitHub issue we sort of decided to make the more relaxed Posix semantics official: I designed it with numpy in mind: Can you elaborate the "complexities"?
On a bit CPU, a word is 8 bytes. If the memory is aligned on 4 bytes, it may have to fetch two words, you loose the advantage of memory alignment.
c++ posix_memalign aligned_alloc - OSX lacks memalign
An integral multiple of alignment" We can add the restriction later, if needed. I wasn't making a criticism of your API; "it's not you, it's us": But this is a complicated and subtle area that's not really part of CPython's core competency, and coming at a time when people are fretting about how to shrink the C APIs surface area. This isn't the Numpy bug tracker, but I can't help but mention that if Numpy grew a facility for users to override the memory allocators it invokes to allocate array data, Numpy may not have to come to a decision about this at all The same argument can be made for any part of the stdlib or core language that PyPy has to reproduce.
I actually have the same problems. For large allocations, you'll probably be better off implementing your own aligned allocator on top of calloc than implementing your own calloc on top of an aligned allocator.
GitHub - NickStrupat/AlignedMalloc: A cross-platform C function to allocate aligned memory
It's O 1 overhead versus O n. Depends on your exact circumstances. Cleanup pymalloc https: If uninitialized or very sparse , the overhead is of course gigantic. I think you're right that a hand-rolled solution on top of calloc is best for my use case. There are lots of reasons to push for other alignments. Maybe not important to you, but there are folks who care. Other places where it can matter is in squeezing out the last bit of performance on large matrix operations or in MPI implementations.
I do this all the time. As I say, it may not matter to you, but there are folks who get real performance boost from considering effects of cache alignment, collisions, and the like. ANd some of us write code that runs under OS X. Might be worthwhile suggesting using Doug Lea's malloc in your code. Post a Comment. Monday, April 11, OSX lacks memalign. From stackoverflow. Quote from the website: Should be easy enough to do yourself, no? Something like the following not tested: From the macosx man pages: Posted by Ku XI at 9: Newer Post Older Post Home.