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The Nvidia GPU architecture has introduced new computing elements such as the\textit{tensor cores}, which are special processing units dedicated to performfast matrix-multiply-accumulate (MMA) operations and accelerate \textit{DeepLearning} applications. In this work we present the idea of using tensor coresfor a different purpose such as the parallel arithmetic reduction problem, andpropose a new GPU tensor-core based algorithm as well as analyze its potentialperformance benefits in comparison to a traditional GPU-based one. The proposedmethod, encodes the reduction of $n$ numbers as a set of $m\times m$ MMAtensor-core operations (for Nvidia's Volta architecture $m=16$) and takesadvantage from the fact that each MMA operation takes just one GPU cycle. Whenanalyzing the cost under a simplified GPU computing model, the result is thatthe new algorithm manages to reduce a problem of $n$ numbers in $T(n) =5\log_{m^2}(n)$ steps with a speedup of $S = \frac{4}{5}\log_2(m^2)$.

Analyzing GPU Tensor Core Potential for Fast Reductions