cross-posted from: https://programming.dev/post/8391233
Dr. Chris Rackauckas (@chrisrackauckas@fosstodon.org) writes:
#julialang GPU-based ODE solvers which are 20x-100x faster than those in #jax and #pytorch? Check out the paper on how #sciml DiffEqGPU.jl works. Instead of relying on high level array intrinsics that #machinelearning libraries use, it uses a direct kernel generation approach to greatly reduce the overhead.
Read Automated translation and accelerated solving of differential equations on multiple GPU platforms
