tadlan

Gotcha!

Thanks. I'm not familiar with dynamic linking. What will that do?

Any specific plans for python support?

Or julia. Which is 1000th better than js

It will eventually, iirc

Question please: Even though there is stated desire to support python etc vm, this will be hampered by the need for the client to download the large VM vs native code that can be distributed itself, correct?

Have you tried dask?

What about dynd? Iirc doesn't use iteration st all.

Woah!!! Cool

Make sure you use the devect macro or parallel accelerator if you're going to use vector iced code :)

It's not a would and gpod. Numba works like that right now, has better interoperability with python and less context switching.

Numba is I easily installed on all systems through conda package manager and is easily used with just a function decorator

Seems you are grasping at straws here.

Actually, Numba array allocation in nopython mode is allowed now.

What other numerical features are missing?

Numba obviate these issues

What would one need to use c or c++ to write a julia package? It's as fast as native code so no need to use multiple languages.

Numba would still be faster. It would fuse away any intermediates in the code and remove any Overhead to the compiled code.

also have the option of devecting to loops.

both of which are generally faster than vectorized jumpy code.

Numbs compiles entire functions on and allows array expressions with allocation and loop fusion. I don't see the problem

Use numba to compile python loops or array expressions to fast llvm, and problem solved. I'm sticking with python.

That's just the data structure. Fitting models would be much tougher.

Will julia ever be popular for general computing and Web stuff? It's s till fast for non scientific use cases, has multi threading (in master) and is getting go like channels.

Julia!!!!! The answer to all these problems, both for general and science programming.

Dask has that, and scikit learn is moving that way also. It even beats spark for out of core work on a single machine

There's a dev branch that has been actively developed

Numba has list, Named tuples, and is working on define your own jit classes.

Numba has loop fusion, runtime, and soon multithreading parallel vectorized functions. How is that c like?

Check out Julia ' s Cxx.jl. it's probably the best c++ FFI in any language. You can even evaluation and call cpp code on the fly from the repl

What was the diet?

That is on the roadmap

Or use pymc 3 for the bayes equivalent. It's Better anyways. Yes one can also use rpy2. I think glmm is a gsoc project or an upcoming pr but not 100% sure.

What are you implying?

What is second generation numba? And any plans to branch numba out of pure numeric application s?