Map-reduce processing of k-means algorithm with FPGA-accelerated computer cluster
Yuk-Ming Choi, Hayden Kwok‐Hay So · 2014
The design and implementation of the k-means clustering algorithm on an FPGA-accelerated computer cluster is presented. The implementation followed the Map-Reduce programming model, with both the map and reduce functions executing autonomously to the CPU on multiple FPGAs. A hardware/software framework was developed to manage gateware execution on multiple FPGAs across the cluster. Using this k-means implementation as an example, system-level tradeoff study between computation and I/O performance in the target multi-FPGA execution environment was performed. When compared to a similar software implementation executing over the Hadoop MapReduce framework, 15.5× to 20.6× performance improvement has been achieved across a range of input data sets.