High Level Programming of Document Classification Systems for Heterogeneous Environments using OpenCL (Abstract Only)

Abstract

Document classification is at the heart of several of the applications that have been driving the proliferation of the internet in our daily lives. The ever growing amounts of data and the need for higher throughput, more energy efficient document classification solutions motivated us to investigate alternatives to the traditional homogenous CPU based implementations. We investigate a heterogeneous system where CPUs are combined with FPGAs as system accelerators. Incorporating FPGAs as accelerators in a heterogeneous computing environment allows for the creation of flexible custom hardware solutions that can potentially offer increased power efficiency and performance gains. One of the main issues delaying wide spread adoption of FPGAs as standard heterogeneous system accelerators is the difficulty in programming them. The OpenCL standard offers a unified C programming model for any device that adheres to its standards. An Altera OpenCL FPGA based implementation of a document classification system is investigated in which a stream of HTML documents is scored according to a profile on a document-by-document basis. The results show that the throughput of the document classification application with and without Bloom Filters is 312MB/s and 343MB/s respectively, when running on CPU, and 354MB/s and 452MB/s respectively, when running on an FPGA. Our results also show up to 32% power efficiency improvement for the FPGA implementation over the CPU implementation. We would like to thank Davor Capalija from Altera for his invaluable advice during our work on the FPGA version of the algorithm.