In this paper, we present ScalParC (Scalable Parallel Classifier), a new parallel formulation of a decision tree based classification process. Like other state-of-the-art decision tree classifiers such as SPRINT, ScalParC is suited for handling large datasets. We show that existing parallel formulation of SPRINT is unscalable, whereas ScalParC is shown to be scalable in both runtime and memory requirements. We present the experimental results of classifying up to 6.4 million records on up to 128 processors of Cray T3D, in order to demonstrate the scalable behavior of ScalParC. A key component of ScalParC is the parallel hash table. The proposed parallel hashing paradigm can be used to parallelize other algorithms that require many concurrent updates to a large hash table.
Joshi, Mahesh; Karypis, George; Kumar, Vipin.
Design of Scalable Parallel Classification Algorithms for Mining Large Datasets.
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