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? 
*Our work is related to medical devices and signal processing.
“Benchmarks don’t lie, but liars do benchmarks.”
I’m afraid you misunderstood the purpose of this bet. 
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
(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:
@code_llvm and look for instructions like 4 x double
--check-bounds=no compiler option, also try --inline=yes
These are the two major tricks that ifort, e.g., does to beat all competitors. If you have arrays, use StaticArrays or tuples, these are immutable. Finally avoid input/output, strings, and parsing files, Julia still is not as good in these.
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 C++.
(Here is an example that combines @evalpoly with symbolic manipulation of continued-fraction expansions in a macro, making it especially easy to beat C++ programs for continued-fraction evaluation: https://github.com/stevengj/18S096/blob/iap2017/pset2/pset2-solutions.ipynb)
I think we’re on the same page here. Someone can definitely match Julia with C++ on any algorithm. The issue is scaling the good algorithms to larger and larger (or more realistic) algorithms. @stevengj’s example is a quick way to do something that, with enough gusto you can get a solution in C++ and match Julia, but the obvious solution is slow and bad. Once your C++ opponent catches up, just scale the amount of the type system and code generation you use and go take a nap.
Julia code complexity scales like O(sqrt(n)) while C++ code complexity scales like O(n^3) (very similar to matrices) where n is the number of lines of code you have to maintain (and there’s an extra effect when build systems get involved). I wouldn’t say C++ can’t do something: just make the point that you don’t want to be the one writing said C++ code.
4 posts were split to a new topic: Salty response to harmless fun question
Ok, I will explain: the purpose is to demonstrate Julia usability and limitations in a concrete context.
I agree, there are other criteria to estimate the usability of Julia for big and complex projects of production-quality. If you know some tutorials or examples of how-to build such projects with Julia, please point me there.
I had some luck in a previous bet with an office mate who was writing some evolutionary algorithm in Matlab. It was a relatively small program, had some nice loops and getindex operations, if-statements introducing branches which can be easily replaced with the branch-free ifelse function. So I accepted the challenge. Half an hour later, bam my program is 8x faster than his Matlab version to the point that he thought I was cheating and using multi-threading 
I told him I can probably make it 3-8 x faster still if I use multi-threading; the code had some embarrassingly parallel loops. He may not have optimized his Matlab code as much as I optimized my Julia code, but he did take more than half an hour figuring out vectorization tricks, so point made…