Multi-GPU approach for big data mining - global induction of decision trees
Krzysztof Jurczuk , Marcin Czajkowski , Marek Krętowski
AbstractThis paper identifies scalability bounds of the evolutionary induced decision trees (DT)s. In order to conquer the barriers concerning the large-scale data we propose a novel multi-GPU approach. It incorporates the knowledge of the global DT induction and EA parallelization. The search for a tree structure and tests is performed sequentially by a CPU, while the fitness calculations are delegated to GPUs, thus the core evolution is unchanged. The results show that the evolutionary induction is accelerated several thousand times by using up to 4 GPUs on datasets with up to 1 billion objects.
|Book||López-ibáñez Manuel (eds.): The Genetic and Evolutionary Computation Conference: GECCO 2019, 2019, Association for Computing Machinery|
|Keywords in English||evolutionary data mining, big data, decision trees, scalability bounds, parallel computing, graphics processing unit (GPU), CUDA|
|Internal identifier||ROC 19-20|
|Score||= 140.0, 04-03-2020, ChapterFromConference|
|Citation count*||1 (2020-04-03)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.