4 ms·

> I honestly don't understand how much slower arrays are. Its like 30x or similar. In what context, under what operation? Depending on context, the difference

> I honestly don't understand how much slower arrays are. Its like 30x or similar. In what context, under what operation? Depending on context, the difference makes sense and is what one would expect - tuples are immutable, with fixed size known at compile time, and stack-allocated; arrays are mutable, dynamic in size, and usually heap-allocated. That's why StaticArrays.jl [1] exists, for when you need something in between/the best of both worlds. > I like Julia but its easy to write slow julia unless you keep the performance tips in mind. I very much agree with this statement though. But following a very few basic ones like "avoid non-constant global variables", "look out for type instabilities", "use @views and @inbounds where it makes sense" gets you most of the way there, for eg. about 3-5x of the time a C program would take. Most of the rest of the tips on the Performance Tips [2] page are to squeeze out the last bits of performance, to go from 5x of C to near-C performance. [1] https://github.com/JuliaArrays/StaticArrays.jl https://github.com/JuliaArrays/StaticArrays.jl [2] https://docs.julialang.org/en/v1/manual/performance-tips/ https://docs.julialang.org/en/v1/manual/performance-tips/

galangalalgol 4y ago
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.

galangalalgol 4y ago

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.