Energy-efficient acceleration of MapReduce applications using FPGAs

In this paper, we present a full end-to-end implementation of big data analytics applications in a heterogeneous CPU+FPGA architecture. Selecting the optimal architecture that results in the highest acceleration for big data applications requires an in-depth of each application. Thus, 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 functions in a non-Java based language suited for interfacing with FPGA-based hardware accelerating environment. We further profile various components of Hadoop MapReduce to identify candidates for hardware acceleration. We accelerate the mapper functions through hardware+software (HW+SW) co-design. Moreover, we study how various parameters at the application (size of input data), system (number of mappers running simultaneously per node and data split size), and architecture (choice of CPU core such as big vs little, e.g., Xeon vs Atom) levels affect the performance and power-efficiency benefits of Hadoop streaming hardware acceleration and the overall performance and energy-efficiency of the system. A promising speedup as well as energy-efficiency gains of up to 8.3× and 15× is achieved, respectively, in an end-to-end Hadoop implementation. Our results show that HW+SW acceleration yields significantly higher speedup on Atom server, reducing the performance gap between little and big cores after the acceleration. On the other hand, HW+SW acceleration reduces the power consumption of Xeon server more significantly, reducing the power gap between little and big cores. Our cost Analysis shows that the FPGA-accelerated Atom server yields execution times that are close to or even lower than stand-alone Xeon server for the studied applications, while reducing the server cost by more than 3×. We confirm the scalability of FPGA acceleration of MapReduce by increasing the data size on 12-node Xeon cluster and show that FPGA acceleration maintains its benefit for larger data sizes on a cluster.