Efficient Parallel Computation of Similarity Matrices from Piecewise Constant Functions

We present a computational framework for efficiently processing piecewise constant functions (PCFs) in parallel on CPUs and GPUs. The framework enables fast computation of similarity matrices, averages, standard deviations, and other statistical measures on large datasets of PCFs.