We present EDDY-GPU, a GPU-accelerated algorithm to identify pathways enriched with differential dependencies between two conditions. High sensitivity has been one benefit of this statistical rigor yet at considerable computational cost, which limits the size of data for EDDY analysis. However, the ample and regular compute, coupled with small memory footprint, positioned EDDY as an ideal candidate for GPU-acceleration. Now complete, EDDY-GPU exhibits two orders of magnitude in performance enhancement. Such improvement provides new opportunities for EDDY-GPU such as 1) TCGA pan-cancer analysis to identify pathways perturbed by multiple mutation compared to wild-type, and 2) personalized target discovery of an individual tumor patient enabled by single cell RNAseq profiles of tumor sample.