Explore GPU-accelerated computation of Vietoris-Rips barcodes and their applications in evaluating deep learning models in this 56-minute conference talk. Delve into the challenges of GPU acceleration after Moore's Law's end, its necessity in deep learning, and the development of Ripser++, a GPU-accelerated software for computing VR barcodes. Discover how Ripser++ has been applied to measure shifts between generated and real data distributions under the manifold hypothesis. Learn about the simplex-wise flag filtration, persistent homology, and efficient persistent pair hashmaps. Investigate the use of topology in deep learning model evaluation, including MTop-Divergence properties and experiments. Examine computational aspects, distribution shift detection, and the application of VR barcodes to attention graphs in BERT models. Gain insights into cutting-edge techniques bridging topological data analysis and machine learning.