On Sun, Feb 16, 2014 at 4:18 PM, <josef.p...@gmail.com> wrote: > On Sun, Feb 16, 2014 at 6:15 PM, <josef.p...@gmail.com> wrote: > > On Sun, Feb 16, 2014 at 5:50 PM, Eelco Hoogendoorn > > <hoogendoorn.ee...@gmail.com> wrote: > >> > >> My guess; > >> > >> First of all, you are actually manipulating twice as much data as > opposed to > >> an inplace sort. > >> > >> Moreover, an inplace sort gains locality as it is being sorted, whereas > the > >> argsort is continuously making completely random memory accesses. > >> > >> > >> -----Original Message----- > >> From: josef.p...@gmail.com > >> Sent: Sunday, February 16, 2014 11:43 PM > >> To: Discussion of Numerical Python > >> Subject: [Numpy-discussion] argsort speed > >> > >> currently using numpy 1.6.1 > >> > >> What's the fastest argsort for a 1d array with around 28 Million > >> elements, roughly uniformly distributed, random order? > >> > >> Is there a reason that np.argsort is almost 3 times slower than np.sort? > >> > >> I'm doing semi-systematic timing for a stats(models) algorithm. > > > > I was using np.sort, inplace sort is only a little bit faster > > > > It looks like sorting first, and then argsorting is faster than argsort > alon > > > > pvals.sort() > > sortind = np.argsort(pvals) > > Ok, that was useless, that won't be anything I want. > > I think locality is the most important thing. The argsort routines used to move both the indexes and the array to argsort, the new ones only move the indexes. It is a tradeoff, twice as many moves vs locality. It's probably possible to invent an algorithm that mixes the two.
<snip> Chuck
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