You may have seen the benchmark results and thought, “how the heck are the Julia ODE solvers on GPUs orders of magnitude faster than the GPU-accelerated Python libraries, that can’t be true?” In this talk I will go into detail about the architectural differences between the Julia approaches to generating GPU-accelerated solvers vs the standard ML library approach to GPU usage. By the end of the talk you’ll have a good enough understanding of models of GPU acceleration to understand why this performance difference exists, and the many applications that can take advantage of this performance improvement.