Egor SmirnovandPhilip Hyunsu Cho 4d6590be3c Optimize ‘hist’ for multi-core CPU (#4529)
* Initial performance optimizations for xgboost

* remove includes

* revert float->double

* fix for CI

* fix for CI

* fix for CI

* fix for CI

* fix for CI

* fix for CI

* fix for CI

* fix for CI

* fix for CI

* fix for CI

* Check existence of _mm_prefetch and __builtin_prefetch

* Fix lint

* optimizations for CPU

* appling comments in review

* add some comments, code refactoring

* fixing issues in CI

* adding runtime checks

* remove 1 extra check

* remove extra checks in BuildHist

* remove checks

* add debug info

* added debug info

* revert changes

* added comments

* Apply suggestions from code review

Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>

* apply review comments

* Remove unused function CreateNewNodes()

* Add descriptive comment on node_idx variable in QuantileHistMaker::Builder::BuildHistsBatch()
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eXtreme Gradient Boosting

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XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.

License

© Contributors, 2016. Licensed under an Apache-2 license.

Contribute to XGBoost

XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone. Checkout the Community Page

Reference

  • Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
  • XGBoost originates from research project at University of Washington.

Sponsors

Become a sponsor and get a logo here. See details at Sponsoring the XGBoost Project. The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).

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NVIDIA

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Other sponsors

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Description
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Readme
33 MiB
Languages
C++ 45.5%
Python 20.3%
Cuda 15.2%
R 6.8%
Scala 6.4%
Other 5.6%