[svantana]

One major issue with Julia is that it only recently reached 1.0, with a lot of breaking changes, that make a lot of libraries incompatible.

Another issue is that it's not always that fast, for a recent project I never managed to exceed 100 MFLOPS, at which point I switched to C++ and got 3 GFLOPS. But the python version stalled out at 4 MFLOPS though...

[cultus]

Did you write type-unstable code? That brings Julia performance down and memory usage up, since things often get inferred to be Any. So, a vector of what you think are doubles could be turned into a boxed vector of Any. It will slow things down to the speed of Python. Fortunately, it's usually pretty easy to avoid this if you are aware of it.

Well-written Julia should always be within a factor of 2-3 of C, often less. Huge problems are done in pure Julia now. Pure Julia code has been run on HPCs to over a petaflop, something that only C/C++ and Fortran have done. 100 MFLOP is not a problem.

[rademacher]

Here is a reference for the comment above and a brief excerpt,

"Written in the productivity language Julia, the Celeste project—which aims to catalogue all of the telescope data for the stars and galaxies in in the visible universe—demonstrated the first Julia application to exceed 1 PF/s of double-precision floating-point performance (specifically 1.54 PF/s)." [1]

[1] https://www.nextplatform.com/2017/11/28/julia-language-deliv...

[stabbles]

Very roughly without knowing details: probably the Julia implementation can be tuned to get close to 3 GFLOPS; it's not that the language has limitations to get above this 100MFLOPS whereas in Python 4MFLOPS might potentially be the best you can get.

Care to share your code and see if it can be improved upon?

[svantana]

I think the main performance bottleneck is that I'm adding to a submatrix. Which seems to be a big performance hit in basically all high level languages.