UC Davis

Cloud vendors such as Amazon (AWS) have started to offer FPGAs in addition to GPUs and CPU in their computing on-demand services. In this work we explore design space trade-offs of implementing a state-of-the-art machine learning library for Gradient-boosted decision trees (GBDT) on Amazon cloud and compare the scalability, performance, cost and accuracy with best known CPU and GPU implementations from literature. Our evaluation indicates that depending on the dataset, an FPGA-based implementation of the bottleneck computation kernels yields a speed-up anywhere from 3X to 10X over a GPU and 5X to 33X over a CPU. We show that smaller bin size results in better performance on a FPGA, but even with a bin size of 16 and a fixed point implementation the degradation in terms of accuracy on a FPGA is relatively small, around 1.3%–3.3% compared to a floating point implementation with 256 bins on a CPU or GPU.