It’s Often Memory That’s Killing Your Performance
My first mistake was trying to make it parallel before I pulled out Instruments. Always start by profiling. Do not make systems parallel before you’ve optimized them serially. Sure enough, the biggest bottleneck was random number generation.
[…]
Huge amounts of time were spent in retain/release. Since there are no classes in this program, that might surprise you, but copy-on-write is implemented with internal classes, and that means ARC, and ARC means locks, and highly contended locks are the enemy of parallelism.
[…]
I rewrote
updateand all the other methods to take two integer parameters rather than one object parameter and cut my time down to 9 seconds [from 40].
pet peeve: using “big-O” to refer to abstract algorithmic complexity. Big-O is the technique of looking at the leading term and ignoring constant factors. It is usually the right tool to analyze memory use or cache misses as well!
Previously:
- Introduction to Move-Only Types in Swift
- Porting Graphing Calculator From C++ to Swift
- Roadmap for Improving Swift Performance Predictability
- Swift Optimization Tips and Reference Counting
