Compact hash tables for decision-trees

Abstract

We provide a conversion technique from decision-trees to compact hash tables.We evaluate the performance on FPGA and multi-core platforms, respectively.We achieve high speedups using our techniques on various platforms. Decision-trees have been widely used in scientific computing. It is challenging to sustain high performance for large decision-trees. In this paper, we present a conversion technique translating a generic decision-tree into multiple compact hash tables; the conversion technique does not depend on the depth or shape of the decision-tree. All the compact hash tables are searched individually; the outcomes from all the tables are merged into the final result. To evaluate the performance, we prototype our design on state-of-the-art FPGA and multi-core General Purpose Processors (GPPs). Experimental results show that, for a typical 92-leaf decision-tree, we achieve 533?Million Classifications Per Second (MCPS) throughput and 26?ns latency on FPGA, and 134?MCPS throughput and 239?ns latency on multi-core GPP. We sustain 6 × and 2.7 × speedups, respectively.