Me and my colleague have made a bet that Julia can be faster than C++ in several test cases. I will write a set of algorithms in Julia, and he will write the same algorithms in C++, then we will measure the performance. My question is: what specific* algorithms should I choose to win? …

“Benchmarks don’t lie, but liars do benchmarks.”

I’m afraid you misunderstood the purpose of this bet. :wink:

Something that vectorizes in Julia with @simd but has a hard time vectorizing without that. Could also try do something that benefits of explicit simd and use SIMD.jl. Hopefully, your opponent won’t resort to simd intrinsics him/herself.

My advice to you would be to steer well clear of arbitrary precision :wink: (at least if you want this to be a beauty contest)

A deep neural net with a diffeq layer on 128-bit floats with confidence intervals embedded. Julia would be faster by default since your opponent wouldn’t be finished coding, so you can grab a beer after finishing in 15 minutes and wait for him to give up.

I think julia`s benefit comes from being a high level language that enormusly helps expressing rather difficult things in C++ very easily at the meantime having comparable performance.

Any heavy numeric code that fulfills these two conditions can easily beat its C++ equivalent: properly gets SIMD vectorized, check with @code_llvm and look for instructions like 4 x double avoids bounds checking, by --check-bounds=no compiler option, also try --inline=yes These are the two majo…

Although it does not answer your question: To make it more interesting, you should not only measure running time, but also development time.

Try something that involves evaluating a complex-valued polynomial, e.g. a power-series approximation to a special function. Your friend will be hard-pressed to beat the code generation of the @evalpoly macro for complex arguments, though of course it is theoretically possible to implement this in…

[image] stevengj: Try something that involves evaluating a complex-valued polynomial, e.g. a power-series approximation to a special function. Your friend will be hard-pressed to beat the code generation of the @evalpoly macro for complex arguments, though of course it is theoretically possible …

A bet: what specific algorithms in Julia can be faster or as fast as C++ implementations?

General Usage Performance

Topic		Replies	Views
Julia vs C++ speed General Usage performance , c	21	5113	September 2, 2021
Making Julia as Fast as C++ Performance	19	1878	May 8, 2026
Performance comparison with C++ Performance	8	2517	October 10, 2019
Julia fn clearer than C++/Fortran (examples sought) General Usage	38	3868	May 30, 2021
Why is Julia faster than C++ for quicksort? Performance performance , quicksort	15	2294	August 15, 2023

A bet: what specific algorithms in Julia can be faster or as fast as C++ implementations?

Related topics