Re: [Numpy-discussion] automatically avoiding temporary arrays
The same idea was published two years ago: http://hiperfit.dk/pdf/Doubling.pdf On Mon, Oct 3, 2016 at 8:53 AM, <numpy-discussion-request@scipy.org> wrote:
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Today's Topics:
1. Re: automatically avoiding temporary arrays (Benjamin Root) 2. Re: Dropping sourceforge for releases. (David Cournapeau) 3. Re: Dropping sourceforge for releases. (Vincent Davis)
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Message: 1 Date: Sun, 2 Oct 2016 09:10:45 -0400 From: Benjamin Root <ben.v.root@gmail.com> To: Discussion of Numerical Python <numpy-discussion@scipy.org> Subject: Re: [Numpy-discussion] automatically avoiding temporary arrays Message-ID: <CANNq6Fn9eGOnrSGz8Duo8J9oTe3N6xTCSqDDP7nRyNqjFKpAjQ@mail. gmail.com> Content-Type: text/plain; charset="utf-8"
Just thinking aloud, an idea I had recently takes a different approach. The problem with temporaries isn't so much that they exists, but rather they they keep on malloc'ed and cleared. What if numpy kept a small LRU cache of weakref'ed temporaries? Whenever a new numpy array is requested, numpy could see if there is already one in its cache of matching size and use it. If you think about it, expressions that result in many temporaries would quite likely have many of them being the same size in memory.
Don't know how feasible it would be to implement though.
Cheers! Ben Root
On Sat, Oct 1, 2016 at 2:38 PM, Chris Barker <chris.barker@noaa.gov> wrote:
Julian,
This is really, really cool!
I have been wanting something like this for years (over a decade? wow!), but always thought it would require hacking the interpreter to intercept operations. This is a really inspired idea, and could buy numpy a lot of performance.
I'm afraid I can't say much about the implementation details -- but great work!
-Chris
On Fri, Sep 30, 2016 at 2:50 PM, Julian Taylor < jtaylor.debian@googlemail.com> wrote:
On 30.09.2016 23:09, josef.pktd@gmail.com wrote:
On Fri, Sep 30, 2016 at 9:38 AM, Julian Taylor <jtaylor.debian@googlemail.com> wrote:
hi, Temporary arrays generated in expressions are expensive as the imply extra memory bandwidth which is the bottleneck in most numpy operations. For example:
r = a + b + c
creates the b + c temporary and then adds a to it. This can be rewritten to be more efficient using inplace operations:
r = b + c r += a
general question (I wouldn't understand the details even if I looked.)
how is this affected by broadcasting and type promotion?
Some of the main reasons that I don't like to use inplace operation in general is that I'm often not sure when type promotion occurs and when arrays expand during broadcasting.
for example b + c is 1-D, a is 2-D, and r has the broadcasted shape. another case when I switch away from broadcasting is when b + c is int or bool and a is float. Thankfully, we get error messages for casting now.
the temporary is only avoided when the casting follows the safe rule, so it should be the same as what you get without inplace operations. E.g. float32-temporary + float64 will not be converted to the unsafe float32 += float64 which a normal inplace operations would allow. But float64-temp + float32 is transformed.
Currently the only broadcasting that will be transformed is temporary + scalar value, otherwise it will only work on matching array sizes. Though there is not really anything that prevents full broadcasting but its not implemented yet in the PR.
This saves some memory bandwidth and can speedup the operation by 50% for very large arrays or even more if the inplace operation allows it
be completed completely in the cpu cache.
I didn't realize the difference can be so large. That would make streamlining some code worth the effort.
Josef
The problem is that inplace operations are a lot less readable so
are often only used in well optimized code. But due to pythons refcounting semantics we can actually do some inplace conversions transparently. If an operand in python has a reference count of one it must be a temporary so we can use it as the destination array. CPython itself does this optimization for string concatenations.
In numpy we have the issue that we can be called from the C-API
where the reference count may be one for other reasons. To solve this we can check the backtrace until the python frame evaluation function. If there are only numpy and python functions in between that and our entry point we should be able to elide the temporary.
This PR implements this: https://github.com/numpy/numpy/pull/7997
It currently only supports Linux with glibc (which has reliable backtraces via unwinding) and maybe MacOS depending on how good their backtrace is. On windows the backtrace APIs are different and I don't know them but in theory it could also be done there.
A problem is that checking the backtrace is quite expensive, so should only be enabled when the involved arrays are large enough for it to be worthwhile. In my testing this seems to be around 180-300KiB sized arrays, basically where they start spilling out of the CPU L2 cache.
I made a little crappy benchmark script to test this cutoff in this branch: https://github.com/juliantaylor/numpy/tree/elide-bench
If you are interested you can run it with: python setup.py build_ext -j 4 --inplace ipython --profile=null check.ipy
At the end it will plot the ratio between elided and non-elided runtime. It should get larger than one around 180KiB on most cpus.
If no one points out some flaw in the approach, I'm hoping to get
to they directly this
into the next numpy version.
cheers, Julian
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