Hi Francesc,
Those numbers are really eye-popping! But the formatting of the code as a
string still bugs me a lot. Asking this as a totally naive user, do you know
whether PEP523 <https://www.python.org/dev/peps/pep-0523/> (adding a frame
evaluation API) would allow numexpr to have a more Pythonic syntax? eg.
with numexpr:
y = np.sin(x) * np.exp(newfactor * x)
?
Juan.
> On 24 Jul 2019, at 6:39 pm, Francesc Alted <fal...@gmail.com> wrote:
>
> (disclosure: I have been a long time maintainer of numexpr)
>
> An important thing to be noted is that, besides avoiding creating big
> temporaries, numexpr has support for multi-threading out of the box and Intel
> VML for optimal evaluation times on Intel CPUs. For choosing your best bet,
> there is no replacement for experimentation:
>
> https://gist.github.com/FrancescAlted/203be8a44d02566f31dae11a22c179f3
> <https://gist.github.com/FrancescAlted/203be8a44d02566f31dae11a22c179f3>
>
> I have no time now to check for memory consumption, but you can expect
> numexpr and Numba consuming barely the same amount of memory. Performance
> wise things are quite different, but this is probably due to my inexperience
> with Numba (in particular, paralellism does not seem to work for this example
> in Numba 0.45, but I am not sure why).
>
> Cheers!
>
>
> Probably numba has this too, but my attempts to use parallelism failed
>
> Missatge de Sebastian Berg <sebast...@sipsolutions.net
> <mailto:sebast...@sipsolutions.net>> del dia dc., 24 de jul. 2019 a les 1:32:
> On Tue, 2019-07-23 at 13:38 -0500, Stanley Seibert wrote:
> > (Full disclosure: I work on Numba...)
> >
> > Just to note, the NumPy implementation will allocate (and free) more
> > than 2 arrays to compute that expression. It has to allocate the
> > result array for each operation as Python executes. That expression
> > is equivalent to:
> >
>
> That is mostly true, although – as Hameer mentioned – on many platforms
> (gcc compiler is needed I think) a bit of magic happens.
>
> If an array is temporary, the operation is replaced with an in-place
> operation for most python operators calls. For example:
> `-abs(arr1 * arr2 / arr3 - arr4)`
> should only create a single new array and keep reusing it in many
> cases[0]. You would achieve similar things with `arr1 *= arr2`
> manually.
>
> Another thing is that numpy will cache some arrays, so that the
> allocation cost itself may be avoided in many cases.
>
> NumPy does no "loop fusing", i.e. each operation is finished before the
> next is started. In many cases, with simple math loop fusing can give a
> very good speedup (which is wher Numba or numexpr come in). Larger
> speedups are likely if you have large arrays and very simple math
> (addition). [1]
>
> As Stanley noted, you probably should not worry too much about it. You
> have `exp`/`sin` in there, which are slow by nature. You can try, but
> it is likely that you simply cannot gain much speed there.
>
> Best,
>
> Sebastian
>
>
> [0] It requires that the shapes all match and that the result arrays
> are obviously temporary.
> [1] For small arrays overheads may be avoided using tools such as
> numba, which can help a lot as well. If you want to use multiple
> threads for a specific function that may also be worth a look.
>
> > s1 = newfactor * x
> > s2 = np.exp(s1)
> > s3 = np.sin(x)
> > y = s3 * s2
> >
> > However, memory allocation is still pretty fast compared to special
> > math functions (exp and sin), which dominate that calculation. I
> > find this expression takes around 20 milliseconds for a million
> > elements on my older laptop, so that might be negligible in your
> > program execution time unless you need to recreate this decaying
> > exponential thousands of times. Tools like Numba or numexpr will be
> > useful to fuse loops so you only do one allocation, but they aren't
> > necessary unless this becomes the bottleneck in your code.
> >
> > If you are getting started with NumPy, I would suggest not worrying
> > about these issues too much, and focus on making good use of arrays,
> > NumPy array functions, and array expressions in your code. If you
> > have to write for loops (if there is no good way to do the operation
> > with existing NumPy functions), I would reach for something like
> > Numba, and if you want to speed up complex array expressions, both
> > Numba and Numexpr will do a good job.
> >
> >
> > On Tue, Jul 23, 2019 at 10:38 AM Hameer Abbasi <
> > einstein.edi...@gmail.com <mailto:einstein.edi...@gmail.com>> wrote:
> > > Hi Ram,
> > >
> > > No, NumPy doesn’t have a way. And it newer versions, it probably
> > > won’t create two arrays if all the dtypes match, it’ll do some
> > > magic to re use the existing ones, although it will use multiple
> > > loops instead of just one.
> > >
> > > You might want to look into NumExpr or Numba if you want an
> > > efficient implementation.
> > >
> > > Get Outlook for iOS
> > >
> > > From: NumPy-Discussion <
> > > numpy-discussion-bounces+einstein.edison=gmail....@python.org
> > > <mailto:gmail....@python.org>> on
> > > behalf of Ram Rachum <r...@rachum.com <mailto:r...@rachum.com>>
> > > Sent: Tuesday, July 23, 2019 7:29 pm
> > > To: numpy-discussion@python.org <mailto:numpy-discussion@python.org>
> > > Subject: [Numpy-discussion] Creating a sine wave with exponential
> > > decay
> > >
> > > Hi everyone! Total Numpy newbie here.
> > >
> > > I'd like to create an array with a million numbers, that has a sine
> > > wave with exponential decay on the amplitude.
> > >
> > > In other words, I want the value of each cell n to be sin(n)
> > > * 2 ** (-n * factor).
> > >
> > > What would be the most efficient way to do that?
> > >
> > > Someone suggested I do something like this:
> > >
> > > y = np.sin(x) * np.exp(newfactor * x)
> > > But this would create 2 arrays, wouldn't it? Isn't that wasteful?
> > > Does Numpy provide an efficient way of doing that without creating
> > > a redundant array?
> > >
> > >
> > >
> > > Thanks for your help,
> > >
> > > Ram Rachum.
> > >
> > > _______________________________________________
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> >
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> --
> Francesc Alted
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