  FPGA-based data processing in datacenters is increasing in popularity due to
the demands of modern workloads and the ensuing necessity for specialization in
hardware. Driven by this trend, vendors are rapidly adapting reconfigurable
devices to suit data and compute intensive workloads. Inclusion of High
Bandwidth Memory (HBM) in FPGA devices is a recent example. HBM promises
overcoming the bandwidth bottleneck, faced often by FPGA-based accelerators due
to their throughput oriented design. In this paper, we study the usage and
benefits of HBM on FPGAs from a data analytics perspective. We consider three
workloads that are often performed in analytics oriented databases and
implement them on FPGA showing in which cases they benefit from HBM: range
selection, hash join, and stochastic gradient descent for linear model
training. We integrate our designs into a columnar database (MonetDB) and show
the trade-offs arising from the integration related to data movement and
partitioning. In certain cases, FPGA+HBM based solutions are able to surpass
the highest performance provided by either a 2-socket POWER9 system or a
14-core XeonE5 by up to 1.8x (selection), 12.9x (join), and 3.2x (SGD).
