You're welcome. I forgot to mention that I'm happy to help if vec_all crashes. So if you give it a shot and get stuck get in touch with me here (or issues on bitbucket)!
Cheers, Richard On 10/20/2015 05:08 PM, Tuom Larsen wrote: > Awesome, thank you! > > On Tue, Oct 20, 2015 at 4:41 PM, Richard Plangger <planri...@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! >>> _______________________________________________ >>> pypy-dev mailing list >>> pypy-dev@python.org >>> https://mail.python.org/mailman/listinfo/pypy-dev >>> >> >> Cheers, >> Richard >> _______________________________________________ >> pypy-dev mailing list >> pypy-dev@python.org >> https://mail.python.org/mailman/listinfo/pypy-dev > _______________________________________________ > pypy-dev mailing list > pypy-dev@python.org > https://mail.python.org/mailman/listinfo/pypy-dev > _______________________________________________ pypy-dev mailing list pypy-dev@python.org https://mail.python.org/mailman/listinfo/pypy-dev
