  Hash tables are used in a plethora of applications, including database
operations, DNA sequencing, string searching, and many more. As such, there are
many parallelized hash tables targeting multicore, distributed, and
accelerator-based systems. We present in this work a multi-GPU hash table
implementation that can process keys at a throughput comparable to that of
distributed hash tables. Distributed CPU hash tables have received
significantly more attention than GPU-based hash tables. We show that a single
node with multiple GPUs offers roughly the same performance as a 500-1,000-core
CPU-based cluster. Our algorithm's key component is our use of multiple
sparse-graph data structures and binning techniques to build the hash table. As
has been shown individually, these components can be written with massive
parallelism that is amenable to GPU acceleration. Since we focus on an
individual node, we also leverage communication primitives that are typically
prohibitive in distributed environments. We show that our new multi-GPU
algorithm shares many of the same features of the single GPU algorithm -- thus
we have efficient collision management capabilities and can deal with a large
number of duplicates. We evaluate our algorithm on two multi-GPU compute nodes:
1) an NVIDIA DGX2 server with 16 GPUs and 2) an IBM Power 9 Processor with 6
NVIDIA GPUs. With 32-bit keys, our implementation processes 8B keys per second,
comparable to some 500-1,000-core CPU-based clusters and 4X faster than prior
single-GPU implementations.
