Parallel Classification on SMP Systems

This paper presents fast scalable decision-tree-based classification algorithms targeting shared-memory systems. The algorithms are based on the sequential SPRINT classifier and span the gamut of data and task parallelism. The data parallelism is based on attribute scheduling among processors. This is extended with task pipelining and dynamic load balancing to yield more efficient schemes. The task parallel approach uses dynamic subtree partitioning among processors. These schemes are disk based and achieve excellent speedup, making them ideally suited for data mining in very large databases.