  Modern data-intensive applications demand high computation capabilities with
strict power constraints. Unfortunately, such applications suffer from a
significant waste of both execution cycles and energy in current computing
systems due to the costly data movement between the computation units and the
memory units. Genome analysis and weather prediction are two examples of such
applications. Recent FPGAs couple a reconfigurable fabric with high-bandwidth
memory (HBM) to enable more efficient data movement and improve overall
performance and energy efficiency. This trend is an example of a paradigm shift
to near-memory computing. We leverage such an FPGA with high-bandwidth memory
(HBM) for improving the pre-alignment filtering step of genome analysis and
representative kernels from a weather prediction model. Our evaluation
demonstrates large speedups and energy savings over a high-end IBM POWER9
system and a conventional FPGA board with DDR4 memory. We conclude that
FPGA-based near-memory computing has the potential to alleviate the data
movement bottleneck for modern data-intensive applications.
