  The cost of DNA sequencing has dropped exponentially over the past decade,
making genomic data accessible to a growing number of scientists. In
bioinformatics, localization of short DNA sequences (reads) within large
genomic sequences is commonly facilitated by constructing index data structures
which allow for efficient querying of substrings. Recent metagenomic
classification pipelines annotate reads with taxonomic labels by analyzing
their $k$-mer histograms with respect to a reference genome database. CPU-based
index construction is often performed in a preprocessing phase due to the
relatively high cost of building irregular data structures such as hash maps.
However, the rapidly growing amount of available reference genomes establishes
the need for index construction and querying at interactive speeds. In this
paper, we introduce MetaCache-GPU -- an ultra-fast metagenomic short read
classifier specifically tailored to fit the characteristics of CUDA-enabled
accelerators. Our approach employs a novel hash table variant featuring
efficient minhash fingerprinting of reads for locality-sensitive hashing and
their rapid insertion using warp-aggregated operations. Our performance
evaluation shows that MetaCache-GPU is able to build large reference databases
in a matter of seconds, enabling instantaneous operability, while popular
CPU-based tools such as Kraken2 require over an hour for index construction on
the same data. In the context of an ever-growing number of reference genomes,
MetaCache-GPU is the first metagenomic classifier that makes analysis pipelines
with on-demand composition of large-scale reference genome sets practical. The
source code is publicly available at https://github.com/muellan/metacache .
