[pypy-dev] Automatic SIMD vectorization

[Tuom Larsen]

Awesome, thank you!

On Tue, Oct 20, 2015 at 4:41 PM, Richard Plangger <planrichi at gmail.com> wrote:
> Hi,
>
> glad you liked the post! See the answers below...
>
> On 10/20/2015 04:20 PM, Tuom Larsen wrote:
>> Hello all!
>>
>> I just read with great interest the blog post "Automatic SIMD
>> vectorization support in PyPy".
>>
>> Please, I have a few questions:
>>
>> - Does regular Python code benefit from the vectorization? I mean, the
>> article on one hand says "it is not specifically targeted for the
>> NumPy library" but on the other it says "Any interpreter (written in
>> RPython)".
>
> Speaking about 'regular' Python code, there is potential !BUT! only if
> enough time is spent in numeric code. What I meant in the article was:
> E.g. you have a vector construct in your language (like the one the R
> language has) you could use the optimization to vectorize operations on
> the variables that represent.
>
> Take a look at my test virtual machine implementing a small subset of R.
> https://bitbucket.org/plan_rich/vecopt-test-vm
>
>>
>> - I would like to write a vector class as much suitable for PyPy as
>> possible, what approach should I take in order to implement it? For
>> example, what would suit PyPy JIT the best:
>>
>>     class Vector3d:
>>         def __init__(a, x, y, z):
>>             (a.x, a.y, a.z) = x, y, z
>>         def __add__(a, b):
>>             return Vector3d(a.x + b.x, a.y+b.y, a.z+b.z)
>>
>>     def add1(a, b):
>>         (ax, ay, az) = a
>>         (bx, by, bz) =  b
>>         return [ax + bx, ay + by, az + bz]
>>
>>    def add2(a, b):
>>         (ax, ay, az) = a
>>         (bx, by, bz) =  b
>>         return (ax + bx, ay + by, az + bz)
>>
>>     def add3((ax, ay, az), (bx, by, bz)):
>>         return (ax + bx, ay + by, az + bz)
>>
>>     def add3:
>>         ???
>>
>
> I have made some tests with this already. You would need to use the
> array module. Python lists would also work, but they leave behind some
> instructions that are not well optimized. It is described in this post:
>
> http://pypyvecopt.blogspot.co.at/2015/08/gsoc-vec-little-brother-of-numpy-array.html
>
> The missing piece is then the --jit vec_all=1 parameter, that you must
> specify on the command line.
>
> Be aware:
>
> 1) vec_all=1 parameter might lead to a crash, I have tested it, but it
> occurred to me that it is not really ready for production. I'm still
> working on this, thus it is disabled by default.
>
> 2) that with a very low number of vector elements (e.g 3) the overhead
> to iterate the first iteration is quite significant. So I do not think
> that there is much you can get just executing 1 vector add in parallel
> on x86.
>
>> - Is NumPyPy going to be included with regular PyPy download/install?
>>
>
> NumPyPy is included in a normal PyPy release version
>
>> Thanks a lot in advance!
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>>
>
> Cheers,
> Richard
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