> Apparently, every computer science problem has easy to understand solution that’s easy to implement but awfully slow in practice.
I have been doing some data structures/algorithms review as I haven't really used much of that knowledge from my CS degree and, frankly, wasn't that great of a student when I was learning it. I've obviously focused mostly on understanding theoretical performance (Big-O) as I've been implementing and playing with things, but I'd like to know about good resources for things to consider about performance on real systems. I hear about things like cache misses, memory layouts and branch mispredictions, but don't really know how to apply that knowledge or, maybe more importantly, measure the effects of optimizing for some of those things over others. I'd appreciate any advice anyone has to offer.
That Big-O thing is mostly theoretical. Most useful application is job interviews.
It might stop you from doing some things, like bubble-sorting 1GB datasets, or inserting many elements to the start of a large array.
However, in other cases it’s just fine to bubble-sort (for very small collections), or insert many elements to the start of the array (when you’re reading much more often so the profit from memory locality while reading outweigh the losses from relocating those elements while inserting).
Modern hardware is extremely complex. No amount of knowledge will help you to 100% predict the performance effects of your changes. Two advices.
(1) Learn to profile your code. Both externally, using a sampling profiler that will tell you where’re you spending the CPU time, and internally using e.g. std::chrono::high_resolution_clock that will tell you the performance effect of your changes.
(2) Don’t forget about rollback feature of your version control, and don’t hesitate using that as necessary. I routinely discover some of my changes I viewed as performance optimizations actually decreased performance. The reasons include branch misprediction, compiler optimization issues, cores competition for L3 cache, RAM bandwidth, thermal issues…
I don’t think engineers working at Ericsson and Apple (the companies listed in the copyright section of that source file) forgot about big-O. What I think happened, when they wrote that, their assumption was people won’t be using those to pass 16MB blobs. And for small messages, O(n^2) works just fine.
I can understand those people. HTTP is better than Web Sockets for large chunks of data. With HTTP, servers and proxies may cache, clients may resume downloads, clients may check for changes with if-modified-since header, and so on.
The problem here isn’t with big-O, it’s with changing world.
If in the future someone will send gigabytes in that event, the current version will break despite O(n), because RAM is finite. Relatively easy to fix, you just need to save data to a hard drive instead or RAM.
Does it mean it’s a good idea to do so now? Don’t think so, it would be over-engineering and premature optimization, and the current version is currently fine. Just like the original O(n^2) code was fine for small messages.
I have been doing some data structures/algorithms review as I haven't really used much of that knowledge from my CS degree and, frankly, wasn't that great of a student when I was learning it. I've obviously focused mostly on understanding theoretical performance (Big-O) as I've been implementing and playing with things, but I'd like to know about good resources for things to consider about performance on real systems. I hear about things like cache misses, memory layouts and branch mispredictions, but don't really know how to apply that knowledge or, maybe more importantly, measure the effects of optimizing for some of those things over others. I'd appreciate any advice anyone has to offer.