uvloop

o that both cases start in the same conditions. [0]: https://github.com/MagicStack/uvloop/

-bound task? I've only encountered this in scientific computing, as I'm a scientist, and for this (as another commenter mentions) I either use numpy[4] (strictly within a numpy call, the GIL is not held) or numba[5] (by default the entirety of your numba-fied functions do not hold the GIL). Therefore, I can either use Python threads or asyncio or Numba's built-in parallelism[6,7] (summary: some numpy routines for working with arrays of values are auto-parallized if called within a numba-fied function, and you can explicitly parallelize your own loops via `for i in numba.prange(N):`; in any case, then decorate the function with `@numba.njit(parallel=True)`) to run these numerical routines in parallel. I easily get 100% of all my CPU's threads doing work this way. I have parallelized numba-fied functions both via numba.prange and via python threads (yes, threads) in the past few weeks to speed up 20-60 minute-long-running codes, and even though I've done this before, I was still very pleasantly surprised at how easy it was, and I got very nearly a 16x speedup on my 16-thread laptop, which made my code practical for interactive data exploration and building up simulations to help me understand some things I was observing, which is where Python really shines for me. Also note that writing a routine in numba provides a straightforward path to putting your numerical codes on GPU[8,9], too, if the that model makes sense for your algorithm (though I've primarily used PyCUDA for this, which worked well). Are you writing something that needs to use distributed (e.g., cluster) based resources? Then dask (dask.distributed) is a great way to go, with a similar interface to the Python standard-library `futures`. Runs all the same code and you get the same forms of parallelism, but distributed across computers. In any case, do you get to FORTRAN or C speeds? Probably not, but close enough, and with the benefits (and costs) of using Python code. [1] talk by Raymond Hettinger: https://www.youtube.com/watch?v=9zinZmE3Ogk [2] asyncio: https://docs.python.org/3/library/asyncio.html [3] uvloop: https://github.com/MagicStack/uvloop [4] numpy: https://scipy-cookbook.readthedocs.io/items/ParallelProgramm... [5] numba: https://numba.pydata.org/ [6] parallelizing numba: overview blog post: https://www.anaconda.com/blog/parallel-python-with-numba-and... [7] parallelizing numba, docs: https://numba.pydata.org/numba-doc/dev/user/parallel.html [8] numba on gpu, example: https://github.com/numba/numba-examples/blob/master/examples... [9] numba on gpu, docs: http://numba.pydata.org/numba-doc/dev/cuda/index.html

bitbashing.io/2015/01/26/d-is-like-native-python.html https://blog.experimentalworks.net/2015/01/the-d-language-a-... If it's been more than a few years since you've evaluated D you might want to check it out again, it may be worth your time. D is a language I knew about for years, and recently is where I've come to appreciate it for it's many features. D has things like array slicing, [OPTIONAL] Garbage Collection, an amazing Web Framework called Vibe.d with it's own template engine called Diet-: http://vibed.org/ https://vibed.org/blog/posts/introducing-diet-ng Things I like is Vibe.d is not just a Web Framework but a full networking stack too, also it supports Pug / Jade like templates (see Diet-NG) you make and compiles them when you compile your project, so your website runs off a native executable using fibers instead of threads. Vibe.d is undergoing a period where the core is being rewritten to where it is more compartmentalized so that you pick and choose which parts you need, MongoDB, PostgreSQL, layout engine and other goodies, there's even a templating library whose syntax is based around eRuby called Temple (though the syntax can be tweaked) that supports Vibe.d: https://github.com/dymk/temple