This is actually really easy. Most C/C++ code is pretty slow. Beating perfectly optimized C/C++ code by a notable margin is basically impossible (all relatively

This is actually really easy. Most C/C++ code is pretty slow. Beating perfectly optimized C/C++ code by a notable margin is basically impossible (all relatively fast languages in the limit tend to converge to theoretical peak CPU performance), but real world code isn't perfectly optimized. The better question is on a performance vs effort graph who wins. Julia has a ton of major advantages here. The base language actually gives you fast implementations of common data structures (e.g. Dictionaries and BitSets) and BLAS/LAPACK wrappers to do linear algebra efficiently while still having your code look like math. The package manager makes it basically trivial to add packages for more complicated problems (no need to mess around with makefiles). the REPL makes it really easy to interactively tweak your algorithms and gives you easy ways to introspect the compilation process (@code_native and friends). Another major advantage is that Julia has macros that make it really easy to make local changes to a block of code's semantics that are compiler flags in C/C++. For example, consider `@fastmath`. In C/C++ you can only opt in to fastmath on a per-compilation unit level, so most projects that have one part that require IEEE handling of nonfinite numbers or require associativity in one part of the program will globally opt out of the non IEEE transforms. In julia, you just write `@fastmath` before a function (or for loop or single line) and you get the optimization.