On Mon, Feb 17, 2014 at 9:42 PM, Nathaniel Smith <n...@pobox.com> wrote: > On 17 Feb 2014 15:17, "Sturla Molden" <sturla.mol...@gmail.com> wrote: >> >> Julian Taylor <jtaylor.deb...@googlemail.com> wrote: >> >> > When an array is created it tries to get its memory from the cache and >> > when its deallocated it returns it to the cache. >> ... > > Another optimization we should consider that might help a lot in the same > situations where this would help: for code called from the cpython eval > loop, it's afaict possible to determine which inputs are temporaries by > checking their refcnt. In the second call to __add__ in '(a + b) + c', the > temporary will have refcnt 1, while the other arrays will all have refcnt >>1. In such cases (subject to various sanity checks on shape, dtype, etc) we > could elide temporaries by reusing the input array for the output. The risk > is that there may be some code out there that calls these operations > directly from C with non-temp arrays that nonetheless have refcnt 1, but we > should at least investigate the feasibility. E.g. maybe we can do the > optimization for tp_add but not PyArray_Add. >
this seems to be a really good idea, I experimented a bit and it solves the temporary problem for this types of arithmetic nicely. Its simple to implement, just change to inplace in array_{add,sub,mul,div} handlers for the python slots. Doing so does not fail numpy, scipy and pandas testsuite so it seems save. Performance wise, besides the simple page zeroing limited benchmarks (a+b+c), it also it brings the laplace out of place benchmark to the same speed as the inplace benchmark [0]. This is very nice as the inplace variant is significantly harder to read. Does anyone see any issue we might be overlooking in this refcount == 1 optimization for the python api? I'll post a PR with the change shortly. Regardless of this change, caching memory blocks might still be worthwhile for fancy indexing and other operations which require allocations. [0] http://yarikoptic.github.io/numpy-vbench/vb_vb_app.html#laplace-normal _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion