[Numpy-discussion] caching large allocations on gnu/linux
jtaylor.debian at googlemail.com
Mon Mar 13 13:11:40 EDT 2017
On 13.03.2017 16:21, Anne Archibald wrote:
> On Mon, Mar 13, 2017 at 12:21 PM Julian Taylor
> <jtaylor.debian at googlemail.com <mailto:jtaylor.debian at googlemail.com>>
> Should it be agreed that caching is worthwhile I would propose a very
> simple implementation. We only really need to cache a small handful of
> array data pointers for the fast allocate deallocate cycle that appear
> in common numpy usage.
> For example a small list of maybe 4 pointers storing the 4 largest
> recent deallocations. New allocations just pick the first memory block
> of sufficient size.
> The cache would only be active on systems that support MADV_FREE (which
> is linux 4.5 and probably BSD too).
> So what do you think of this idea?
> This is an interesting thought, and potentially a nontrivial speedup
> with zero user effort. But coming up with an appropriate caching policy
> is going to be tricky. The thing is, for each array, numpy grabs a block
> "the right size", and that size can easily vary by orders of magnitude,
> even within the temporaries of a single expression as a result of
> broadcasting. So simply giving each new array the smallest cached block
> that will fit could easily result in small arrays in giant allocated
> blocks, wasting non-reclaimable memory. So really you want to recycle
> blocks of the same size, or nearly, which argues for a fairly large
> cache, with smart indexing of some kind.
The nice thing about MADV_FREE is that we don't need any clever cache.
The same process that marked the pages free can reclaim them in another
allocation, at least that is what my testing indicates it allows.
So a small allocation getting a huge memory block does not waste memory
as the top unused part will get reclaimed when needed, either by numpy
itself doing another allocation or a different program on the system.
An issue that does arise though is that this memory is not available for
the page cache used for caching on disk data. A too large cache might
then be detrimental for IO heavy workloads that rely on the page cache.
So we might want to cap it to some max size, provide an explicit on/off
switch and/or have numpy IO functions clear the cache.
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