| This is one of those questions that is extremely hard to predict. Even though I was part of that team, it doesn't mean I have any special insight into what will happen. A lot will depend on sources of funding. Will folks like Nvidia start sponsoring Numba, and if so what will it mean for support of Nvidia alternatives like OpenCL? It seems like NumbaPro as a stand-alone for-pay product is not viable on its own, but that claim could be wrong based on more recent data that I don't have access to. So external sponsorship may be necessary. One form of this could be through Continuum's already established business model of consulting and support services. But then the question is whether the nature of those consulting and support projects will allow for developers to actually further the cause of Numba, or just merely hack in poorly conceived features that are demanded by the consulting and support customers? Since Numba is open source, it should be easy enough for anyone to follow along with commits and discussions on GitHub and make their own opinion about what direction that is going. The other question that is always hard is staffing. Far and away the colleagues I had the chance to work with on the Numba team were amazingly good. But it's not clear if working solely on Numba can justify the sort of salary that would be required to attract very top engineers and grow the team. You might start to see more interns and/or post-doc type labor feeding into Numba, and again I don't know what that will mean for the project ... could be good or bad. At the same time, you've also got a lot of active development for Julia, PyPy, and a lot of people still prefer to use Cython rather than jitting functions. Some people even call into question the entire goal of making something that is "easy" but also a "black box" -- like the way just dropping in `jit` works for people who merely use, but don't understand the inner workings of, CPython. It's an exciting area, and the Numba team has as much talent and ability to claim a significant piece of the tool space surrounding high performance computing as anyone else. Whether that will pan out for them is still really hard to predict. |
What do you think about the foundational tech of numba itself?
Do you think it is any more of a black box than say Julia? Is there anything about it that would impede extension into a more stable, predictable and feature rich product?
BTW looks like Intel is doing some stuff with Julia: https://github.com/IntelLabs/ParallelAccelerator.jl
There has also been alot of recent funding to Continuum and dev of numba seems to be going strong. Also some recent work with AMD.