Big data analytics on heterogeneous accelerator architectures

In this paper, we present the implementation of big data analytics applications in a heterogeneous CPU+FPGA accelerator architecture. We develop the MapReduce implementation of K-means, K nearest neighbor, support vector machine and Naive Bayes in a Hadoop Streaming environment that allows developing mapper/reducer functions in a non-Java based language suited for interfacing with FPGA-based hardware accelerating environment. We present a full implementation of the HW+SW mappers on the Zynq FPGA platform. A promising speedup as well as energy-efficiency gains of upto 4.5X and 22X is achieved, respectively, in an end-to-end Hadoop implementation.