  Graph plays a vital role in representing entities and their relationships in
a variety of fields, such as e-commerce networks, social networks and
biological networks. Given two vertices s and t, one of the fundamental
problems in graph databases is to investigate the relationships between s and
t. A well-studied problem in such area is k-hop constrained s-t simple path
enumeration. Nevertheless, all existing algorithms targeting this problem
follow the DFS-based paradigm, which cannot scale up well. Moreover, using
hardware devices like FPGA to accelerate graph computation has become popular.
Motivated by this, in this paper, we propose the first FPGA-based algorithm
PEFP to solve the problem of k-hop constrained s-t simple path enumeration
efficiently. On the host side, we propose a preprocessing algorithm Pre-BFS to
reduce the graph size and search space. On the FPGA side in PEFP, we propose a
novel DFS-based batching technique to save on-chip memory efficiently. In
addition, we also propose caching techniques to cache necessary data in BRAM,
which overcome the latency bottleneck brought by the read/write operations
from/to FPGA DRAM. Finally, we propose a data separation technique to enable
dataflow optimization for the path verification module; hence the sub-stages in
that module can be executed in parallel. Comprehensive experiments show that
PEFP outperforms the state-of-the-art algorithm JOIN by more than 1 order of
magnitude by average, and up to 2 orders of magnitude in terms of preprocessing
time, query processing time and total time, respectively.
