Its have to do with
parallelism implementation of XGBoost
I am trying to optimize XGBoost execution by giving it parameter nthread = 16 where my system has 24 cores. But when I train my model, it doesn't seem to even cross approx 20% of CPU utilization at any point in time while model training. Code snippet is as follows:-
param_30 <- list("objective" = "reg:linear", # linear
"subsample"= subsample_30,
"colsample_bytree" = colsample_bytree_30,
"max_depth" = max_depth_30, # maximum depth of tree
"min_child_weight" = min_child_weight_30,
"max_delta_step" = max_delta_step_30,
"eta" = eta_30, # step size shrinkage
"gamma" = gamma_30, # minimum loss reduction
"nthread" = nthreads_30, # number of threads to be used
"scale_pos_weight" = 1.0
)
model <- xgboost(data = training.matrix[,-5],
label = training.matrix[,5],
verbose = 1, nrounds=nrounds_30, params = param_30,
maximize = FALSE, early_stopping_rounds = searchGrid$early_stopping_rounds_30[x])
Please explain me ( if possible ) on how I can increase CPU utilization and speed up the model training for efficient execution. Code in R shall be helpful for further understanding.
Assumption:- It is about the execution in R package of XGBoost
This is a guess... but I have had this happen to me...
You are spending to much time communicating during the parallelism and are not ever getting CPU bound. https://en.wikipedia.org/wiki/CPU-bound
Bottom line is your data isn't large enough (rows and columns ), and/or your trees aren't deep enough max_depth
to warrant that many cores. Too much overhead. xgboost
parallelizes split evaluations so deep trees on big data can keep the CPU humming at max.
I have trained many models where single threaded outperforms 8/16 cores. Too much time switching and not enough work.
**MORE DATA, DEEPER TREES OR LESS CORES:) **
I tried to answer this question but my post was deleted by a moderator. Please see https://stackoverflow.com/a/67188355/5452057 which I believe could help you also, it relates to missing MPI support in the xgboost R-package for Windows available from CRAN.
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