  Tensor cores (TCs) are a type of Application-Specific Integrated Circuit
(ASIC) and are a recent addition to Graphics Processing Unit (GPU)
architectures. As such, TCs are purposefully designed to greatly improve the
performance of Matrix Multiply-Accumulate (MMA) operations. While TCs are
heavily studied for machine learning and closely related fields, where their
high efficiency is undeniable, MMA operations are not unique to these fields.
More generally, any computation that can be expressed as MMA operations can
leverage TCs, and potentially benefit from their higher computational
throughput compared to other general-purpose cores, such as CUDA cores on
Nvidia GPUs. In this paper, we propose the first double precision (FP64)
Euclidean distance calculation algorithm, which is expressed as MMA operations
to leverage TCs on Nvidia GPUs, rather than the more commonly used CUDA cores.
To show that the Euclidean distance can be accelerated in a real-world
application, we evaluate our proposed TC algorithm on the distance similarity
self-join problem, as the most computationally intensive part of the algorithm
consists of computing distances in a multi-dimensional space. We find that the
performance gain from using the tensor core algorithm over the CUDA core
algorithm depends weakly on the dataset size and distribution, but is strongly
dependent on data dimensionality. Overall, TCs are a compelling alternative to
CUDA cores, particularly when the data dimensionality is low ($\leq{4}$), as we
achieve an average speedup of $1.28\times$ and up to $2.23\times$ against a
state-of-the-art GPU distance similarity self-join algorithm. Furthermore,
because this paper is among the first to explore the use of TCs for FP64
general-purpose computation, future research is promising.
