FARMER: An Online-Learning Driven Methodology for Workload Consolidation on Large FPGAs

With the ever-increasing demand for performance and scalability in cloud applications, high-performance computing (HPC) facilities are starting to include FPGAs for workload acceleration. To efficiently exploit the massive amount of resources of high-end FPGAs, it is paramount to optimize the allocation of multiple applications on a single device. This paper proposes FARMER, a novel online learning methodology harnessing the power of Gaussian Process regression to model the throughput of different applications running on the same FPGA, and a sequential decision-making approach to explore the available configurations efficiently. Experimental results considering a large variety of representative scenarios tested on a real prototyping platform featuring an AMD Virtex-7 FPGA show that FARMER always finds a feasible solution with an exploration of less than 0.1% of the whole design space.