I'm a pretty competent C programmer, but doing even fairly simple matrix programming in C is fiendishly hard not to screw up. When I was porting this code [1] to this [2], it took an absurdly long time to iron out all the bugs. And just look at the difference in verbosity and difficulty. Worst of all, C has no guard rails: it's so easy to think you have it right, only to realize much later (or not at all, frankly) that you're computing complete nonsense.
That C code is 39% faster than the Julia version, so faster but not massively so. You can optimize the Julia code to be as fast as the C code by using in-place operations, but it's probably not worth if for such a small speedup unless the code is a real bottleneck. By comparison, the Python version (using NumPy, of course) is 25x slower than C, which is a whole different situation.
Most the speed advantage of the C here is due to reusing the same memory over and over, which you can do pretty easily in Julia as well. Here's the Julia code using in-place operations and it's still quite a bit more readable than the C version: https://gist.github.com/StefanKarpinski/e57f5a36b7890b261a0d.... I timed this and it's the same speed as the C version. When developing this, it follows the same general outline as the C version, but you have several benefits: (1) You can use asserts to compare to the easy version; (2) There are niceties like bounds checks and array indexing. In C you can't do (1) because there is on easy version to compare with. And doing the array index computations in C is kind of a nightmare—it's so easy to accidentally screw them up.
[1] https://github.com/JuliaLang/Microbenchmarks/blob/af3d18f7b3...
[2] https://github.com/JuliaLang/Microbenchmarks/blob/af3d18f7b3...