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Parallelism

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

Parallelization is the execution of independent computational operations concurrently across multiple processing resources.

Parallelization executes independent computations concurrently across processing resources. In XGBoost, column blocks divide features so each boosting model can use multiple CPU cores, reducing training time. section 5.4.2

Use the distinction between parallel work and sequential model construction:

  • Parallelize split-finding across feature columns and processor cores within each model. section 5.4.2
  • Keep boosting iterations sequential because each model uses the previous model’s results. section 5.4.2
  • Do not confuse this with training multiple independent models simultaneously, as in random forests. section 5.4.2

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes
Explains column blocks and serial boosting, but stops short of detailing scheduling or split-finding algorithms.
Examples
Uses XGBoost CPU utilization and column-block training as concrete examples of parallel execution.
What you can do after
Operate XGBoost through its Scikit-learn API while benefiting from internal multi-core training.
How it connects
Connects parallelism with XGBoost, gradient boosting, random forests, CPU cores, and column blocks.
Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
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This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.