Fast Min-plus Convolution and Deconvolution on GPUs.

The min-plus convolution and deconvolution operations are frequently needed in the network calculus for computing performance metrics. However, due to their computational complexity, the operations can become impractical with large data sets. We provide a GPU-accelerated implementation that achieves a 400x speedup for a data set with a half million data points, reducing the computation time from more than an hour to about 10 seconds. We also determine the maximum ranges for which the convolution and deconvolution of data traces finite support can be correctly computed.