Runtime support for parallelizing data mining algorithms

Ruoming Jin; Gagan Agrawal

doi:10.1117/12.460230

12 March 2002 Runtime support for parallelizing data mining algorithms

Ruoming Jin, Gagan Agrawal

Proceedings Volume 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV; (2002) https://doi.org/10.1117/12.460230
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

With recent technological advances, shared memory parallel machines have become more scalable, and offer large main memories and high bus bandwidths. They are emerging as good platforms for data warehousing and data mining. In this paper, we focus on shared memory parallelization of data mining algorithms. We have developed a series of techniques for parallelization of data mining algorithms, including full replication, full locking, fixed locking, optimized full locking, and cache-sensitive locking. Unlike previous work on shared memory parallelization of specific data mining algorithms, all of our techniques apply to a large number of common data mining algorithms. In addition, we propose a reduction-object based interface for specifying a data mining algorithm. We show how our runtime system can apply any of the technique we have developed starting from a common specification of the algorithm.

Citation Download Citation

Ruoming Jin and Gagan Agrawal "Runtime support for parallelizing data mining algorithms", Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); https://doi.org/10.1117/12.460230

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