You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
another - with 8-core processor AMD Ryzen 7 3700X
Both CPUs have almost the same single thread rating (based on passmark website - see the links above), while multiple thread rating is more than twice better for Ryzen 7.
I am running the same code on the same data on both PCs. The code does hyperparameter search using Optuna, and it trains XGBoost model. Optuna measures the time for each single iteration, so that I could build a histogram of the time for model training, and this is what I see:
I was not a surprise for me that on both PCs Optuna could perform about 1500 operations: while there are twice more cores for Ryzen than for core i5, the speed of XGBoost training is twice slower, and as a result, we are getting the same number of iterations.
I tried to recompile XGBoost with different optimization flags under ryzen:
march=native, march=znver2
O3
flto
mavx2
mfma
But this all just doesn't help.
The text was updated successfully, but these errors were encountered:
There are some ad-hoc internal constants that can be tuned scattered around the code base. If you are interested in this, I can try to gather them into one place.
Please take a look at: #9694tuning.h. Considering the AMD is known for stuffing large cache in their CPUs, you might want to increase some of the parameters.
I have XGBoost 2.0.0 installed on two machines:
Both CPUs have almost the same single thread rating (based on passmark website - see the links above), while multiple thread rating is more than twice better for Ryzen 7.
I am running the same code on the same data on both PCs. The code does hyperparameter search using Optuna, and it trains XGBoost model. Optuna measures the time for each single iteration, so that I could build a histogram of the time for model training, and this is what I see:
I was not a surprise for me that on both PCs Optuna could perform about 1500 operations: while there are twice more cores for Ryzen than for core i5, the speed of XGBoost training is twice slower, and as a result, we are getting the same number of iterations.
I tried to recompile XGBoost with different optimization flags under ryzen:
But this all just doesn't help.
The text was updated successfully, but these errors were encountered: