Hey guys, looking to benefit from the communities’ wisdom here and possibly spark a bit of discussion.
Short version: does anyone know if the training time of CPU implementations of tabular learning algorithms (XGBoost in particular but also LightGBM, TabNet, etc) depend on RAM speeds?
Longer version. I recently switched from an i7 12700KF CPU to an i9 13900K. Doing a somewhat heavy AutoGluon training (most time spent in the algorithms above) that takes 4 hours I got a 1.6X speedup from the newer processor which is great, training now takes 2.5 hours so more trials per day of work). My RAM is a 2x32GB kit of DDR4 memory that can work overclocked at 3200MHz. However while installing the new CPU it defaulted back to 2133MHz. At that speed, training was far slower, I don’t recall the exact figure but something like 50% as fast. After overclocking to 3200MHz, the 1.6X speedup.
There’s thousands of RAM benchmarks for games (where RAM speeds have a limited impact) but I’ve found none for ML. Closest I got was this video from LTT https://www.youtube.com/watch?v=b-WFetQjifc where he shows for some productivity apps it has a major impact but none of those are ML applications.
So my question is: are these algorithms training times sensitive to RAM bandwidth? More so for CPUs with higher core counts?