Analyzing GPU Tensor Core Potential for Fast Reductions
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The Nvidia GPU architecture has introduced new computing elements such as the tensor cores, which are special processing units dedicated to perform fast matrix-multiply-accumulate (MMA) operations and accelerate Deep Learning applications. In this work we present the idea of using tensor cores for a different purpose such as the parallel arithmetic reduction problem, and propose a new GPU tensor-core based algorithm as well as analyze its potential performance benefits in comparison to a traditional GPU-based one. The proposed method, encodes the reduction of n numbers as a set of m× m MMA tensor-core operations (for Nvidia's Volta architecture m=16) and takes advantage from the fact that each MMA operation takes just one GPU cycle. When analyzing the cost under a simplified GPU computing model, the result is that the new algorithm manages to reduce a problem of n numbers in T(n) = 5_m^2(n) steps with a speedup of S = 4/5_2(m^2).
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