[galdauts]

We have been running benchmarks to compare different languages relevant to high-performance computing and unfortunately Julia still lags behind even Numba-JIT-compiled Python. Perhaps my understanding of Julia is limited, but even the Rodinia SRAD program, which was originally written in Julia, performs faster in all other implementations that aren't Julia.

[hatmatrix]

Do you have an idea whether these are specific types of problems that is giving Julia poorer performance? From what I recall, people were reporting better speeds with Julia than with Numba (e.g., [1]). My impression was that you are basically able to bring more of your code to LLVM with Julia than Numba, so it would make sense.

[1] https://gerritnowald.wordpress.com/2022/10/03/simulating-rot...

[galdauts]

Thank you for the article! We're mainly interested in floating-point performance and energy consumption w/r/t to solving differential equations and tridiagonal systems of equations, while running on a 128-core compute node. Our current results will likely only be presented in May, but here are last year's results: https://www.cs.uni-potsdam.de/bs/research/docs/papers/2025/l...

Our Julia code is parallelised with FLoops.jl, but so far Numba has shown surprising performance benefits when executing code in parallel, despite being slower when executed sequentially. Therefore I can imagine that Julia might yield better results when run in a regular desktop environment.

[wolvesechoes]

I don't have much experience with Numba, but Julia, contrary to some online claims, is not really "reads like Python, runs like C" language. When it is able to run like C, it doesn't look as clean as Python, and vice versa.

I am not familiar with Julia nor Numba internals, but maybe Numba, due to being more specialized, can actually provide LLVM with info that allows it to make more aggressive optimizations more easily.

[ForceBru]

I found this paper (https://www.cs.uni-potsdam.de/bs/research/docs/papers/2025/l...) from around 2025 (it cites papers from 2025) which shows that the Julia version of SRAD (along with some other benchmarks) is about 5 times slower than the slowest FORTRAN implementation and consumes at least 5 times more energy, see Table 4 and Figure 1. This paper, however, doesn't seem to be peer-reviewed.

[galdauts]

Yes, that's the paper my predecessors worked on! I replicated the measurements with an upgraded version of Julia (1.12), but despite the claimed performance benefits, Julia still performed poorly.