  Cellular automata (CA) are simulation models that can produce complex
emergent behaviors from simple local rules. Although state-of-the-art GPU
solutions are already fast due to their data-parallel nature, their performance
can rapidly degrade in CA with a large neighborhood radius. With the inclusion
of tensor cores across the entire GPU ecosystem, interest has grown in finding
ways to leverage these fast units outside the field of artificial intelligence,
which was their original purpose. In this work, we present CAT, a GPU tensor
core approach that can accelerate CA in which the cell transition function acts
on a weighted summation of its neighborhood. CAT is evaluated theoretically,
using an extended PRAM cost model, as well as empirically using the Larger Than
Life (LTL) family of CA as case studies. The results confirm that the cost
model is accurate, showing that CAT exhibits constant time throughout the
entire radius range $1 \le r \le 16$, and its theoretical speedups agree with
the empirical results. At low radius $r=1,2$, CAT is competitive and is only
surpassed by the fastest state-of-the-art GPU solution. Starting from $r=3$,
CAT progressively outperforms all other approaches, reaching speedups of up to
$101\times$ over a GPU baseline and up to $\sim 14\times$ over the fastest
state-of-the-art GPU approach. In terms of energy efficiency, CAT is
competitive in the range $1 \le r \le 4$ and from $r \ge 5$ it is the most
energy efficient approach. As for performance scaling across GPU architectures,
CAT shows a promising trend that if continues for future generations, it would
increase its performance at a higher rate than classical GPU solutions. The
results obtained in this work put CAT as an attractive GPU approach for
scientists that need to study emerging phenomena on CA with large neighborhood
radius.
