The whole computer hardware industry embraced multicores. For these machines, the extreme optimisation of sequential algorithms is no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an approach for easy-yet-efficient porting of an implementation of the C4.5 algorithm on multicores. The parallel porting requires minimal changes to the original sequential code, and it is able to exploit up to 7X speedup on an Intel dual-quad core machine.

Porting decision tree algorithms to multicore using fastflow

ALDINUCCI, MARCO;
2010-01-01

Abstract

The whole computer hardware industry embraced multicores. For these machines, the extreme optimisation of sequential algorithms is no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an approach for easy-yet-efficient porting of an implementation of the C4.5 algorithm on multicores. The parallel porting requires minimal changes to the original sequential code, and it is able to exploit up to 7X speedup on an Intel dual-quad core machine.
2010
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010
Barcelona, Spain
September 2010
Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010
Springer-Verlag
6321
7
23
364215879X
9783642158797
http://www.springerlink.com/content/x20020l947041770/
parallel classification; C4.5; multicores; structured parallel programming; streaming
Marco Aldinucci; Salvatore Ruggieri; Massimo Torquati
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/83743
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