May 9, 2026
Fast talk, slower feelings
Making Julia as Fast as C++
Julia tried to beat C++, and the comments turned into a roast battle
TLDR: A blog tried to show Julia can match C++ speed in heavy scientific code, but readers zeroed in on the awkward tradeoff: making Julia fast seemed to mean making it look like C++. The comments split between “this proves Julia’s limits” and “you’re judging it by a very old version,” with plenty of roasting in between.
A blog post set out to tackle a classic nerd dream: can Julia, a language beloved for being easier to write than C++, be pushed to run just as fast? The author walked through a real-world physics problem, squeezing out speed with every trick available. But in the comment section, the real action wasn’t the math — it was the collective side-eye.
The sharpest reaction came from readers who basically said: if you have to twist Julia until it looks like ugly C++ anyway, what’s the point? One commenter delivered the knockout line, saying the code was rewritten to look almost identical to C++, with safety features switched off and extra compiler hints piled on — and it was still slower. Ouch. That turned the thread into a mini identity crisis for Julia fans: is this proof Julia can get close, or proof that “easy and fast” still comes with fine print?
Others pushed back more gently, noting this was based on Julia 1.0.3 from 2019, ancient by internet standards, and arguing it reveals more about an old compiler than the language today. Then came the most meta comment of all: someone groaned that this was a 7-year-old post about a 10-year-old language, joking that it had become perfect bait for empty AI-generated takes. Even the link to a newer Julia Discourse thread felt like the fandom arriving late to clean up the mess. In other words: the benchmark debate was spicy, but the comments were the real sport.
Key Points
- •The article examines Julia performance optimization through a high-performance computing case study in aerodynamics.
- •Its example problem is the vortex particle method, where induced velocity and velocity Jacobian are computed for a field of particles.
- •Because particles also evaluate the flow they induce on themselves, the computation becomes an O(N^2) N-body problem.
- •The post defines the governing equations using the Newtonian kernel, a smoothing function, and particle positions and strengths.
- •A C++ particle-to-particle implementation is shown as the concrete computational baseline for the optimization discussion.