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Also, I might not have been clear: we are very interested in a PR to add PyPy support and will put the appropriate testing into our CI system. :)
On Thu, Jan 19, 2017 at 9:04 AM, Stanley Seibert <stan.s...@continuum.io> wrote:
Hi Graham,This is very exciting to see! I've wondered whether PyPy could use Numba as a final-stage optimizer for numerical code, and your patches would provide the framework for those experiments. Additionally, we're starting to see a few packages achieve high performance entirely through Python + Numba (for example, the fastparquet project is using Numba for all of its speed critical code), so having Numba work in both CPython and PyPy means that package authors can achieve more portable performance between the two runtimes.(That said, fastparquet specifically also depends on pandas, so it may not be the best example at the moment, unless pandas also works in PyPy now.)We're also in the process of upgrading to LLVM 3.9 (with LLVM 4.0 likely coming shortly after its official release). There should be some major improvements to SIMD code generation in those releases, so it is possible that they will produce more efficient code than PyPy's backend on some numerical functions. That might also provide a win.One last question: Any thoughts on what it would take to support the CUDA target as well? It would be very awesome to have a way to run GPU code from within PyPy.
On Thu, Jan 19, 2017 at 8:28 AM, <graham....@embecosm.com> wrote:
Hi,
I've recently been working on getting Numba to run on PyPy, with some success; my current branch has 91.5% of Numba tests passing on PyPy, so most things are working. From the set of experiments that I've done, there appears to be a small overhead of using Numba on PyPy compared to CPython - I suspect that this is a bit of cpyext overhead that would mostly be amortised for jitted functions that have a reasonable workload.
A summary of the changes I needed to make and some test results and performance experiments are described in: http://www.embecosm.com/2017/01/19/running-numba-on-pypy/
I'd be interested in feedback on this - in particular, if there are interesting use cases for running Numba on PyPy, and whether the work required to improve the patches would be worthwhile.
Best regards,
Graham.
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Hi Graham,This is very exciting to see! I've wondered whether PyPy could use Numba as a final-stage optimizer for numerical code, and your patches would provide the framework for those experiments.
Additionally, we're starting to see a few packages achieve high performance entirely through Python + Numba (for example, the fastparquet project is using Numba for all of its speed critical code), so having Numba work in both CPython and PyPy means that package authors can achieve more portable performance between the two runtimes.(That said, fastparquet specifically also depends on pandas, so it may not be the best example at the moment, unless pandas also works in PyPy now.)
We're also in the process of upgrading to LLVM 3.9 (with LLVM 4.0 likely coming shortly after its official release). There should be some major improvements to SIMD code generation in those releases, so it is possible that they will produce more efficient code than PyPy's backend on some numerical functions. That might also provide a win.
One last question: Any thoughts on what it would take to support the CUDA target as well? It would be very awesome to have a way to run GPU code from within PyPy.
Also, I might not have been clear: we are very interested in a PR to add PyPy support and will put the appropriate testing into our CI system. :)
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