I had been using arrays the way I'd use vectors in c++ or arrays in matlab. For DSP type tasks you typically allocate a tensor on the heap, the size can be cons

I had been using arrays the way I'd use vectors in c++ or arrays in matlab. For DSP type tasks you typically allocate a tensor on the heap, the size can be constant and even hardcoded, which allows lots of optimization. But when I modified the nbody implementation from the benchmark game from tuples to arrays for the position and velocity vectors it went from 5.3s to 3min+. That was with specifying the type and initializing them as part of a struct all at once.... And I just realized my mistake, everytime I made a new struct it was allocating a new array with all the overhead that entails. Simply ditching the structs for a single array and allowing them to mutate would have been more idiomatic to the way I use matlab and it would have avoided all that reallocation.