Zeyna & SnapFitSoul
SnapFitSoul
I was thinking about those micro‑optimizations that slip through code reviews—like when a recursive depth‑first search ends up quadratic by accident. How do you normally hunt down those hidden bottlenecks?
Zeyna
Run a profiler first – see where the time is really spent. Then step back and think about the algorithm’s theoretical complexity. With a DFS that becomes quadratic you’re usually recomputing the same sub‑problem over and over. Spot that by looking for repeated calls with the same arguments or nested loops inside the recursion. Add memoization, or better yet rewrite the routine iteratively so you keep a stack of nodes instead of calling yourself again. Also watch for needless copies of large structures in each call – that can drive the cost up too. After you tweak it, re‑profile with a bigger dataset to make sure the hotspot is gone. That’s the systematic way to hunt down those hidden bottlenecks.
SnapFitSoul
Sounds like a textbook recipe, but I’d also add a sanity check: before you dive into memoization, confirm the recurrence really is redundant. If the branching factor is constant, the quadratic blow‑up must come from elsewhere—maybe the data structure you’re copying each recursion step. A quick static analysis can catch those hidden O(n) copies before you waste time rewriting the algorithm. And if you do rewrite iteratively, make sure you don’t introduce a hidden O(n²) stack growth by storing too much per frame. In short, profile, hypothesize, then verify your hypothesis before you commit the patch.
Zeyna
That’s a solid approach. Double‑check the data structures for hidden linear copies before you invest in memo. Static checks are cheap, and you’ll catch most O(n) blow‑ups early. And when you move to an iterative stack, keep the frame size tight—those hidden O(n²) growths are easy to slip in. So profile, hypothesize, verify, repeat. Got a particular case you’re wrestling with right now?
SnapFitSoul
I’m actually working on a graph‑traversal routine that keeps re‑allocating adjacency lists inside a loop, so it’s a textbook linear copy that’s been masked by a recursive wrapper. I’m profiling it, but I’m still hunting that exact line where the vector is built from scratch each time. It’s a perfect test case for the approach you just outlined.
Zeyna
Sounds like the classic “build‑copy‑in‑loop” trap. Pin it down by turning the loop into a single expression that returns a pre‑allocated vector, then swap the copy with a move. If you’re using a language with move semantics, just push the existing adjacency list into a new vector instead of copying. Or, better yet, build the adjacency list once outside the loop and reuse it. Once you swap out the copy, the profiler should drop that line to zero. Give it a shot.
SnapFitSoul
So you want me to rewrite a loop that rebuilds an adjacency list every iteration into a single move. Fine, but if I’m wrong and the vector’s size changes inside the loop, the move will still copy the new elements. Also, be careful with the ownership semantics—moving a vector into a new one isn’t a free lunch if you later need to read from the original. In practice I usually just pre‑allocate once, push back, and if I really need to keep the old data, clone that specific slice. It keeps the profiler happy and the code honest.