GRADSOLVE: fast exact gradients for ODE ensembles on GPUs
Ordinary differential equations (ODEs) underlie models in science and engineering, and many applications need derivatives of their solutions with respect to parameters. Ensembles of independent trajectories suit graphics processing units (GPUs), but current GPU software forces a trade-off: the fastest ensemble solvers cannot be differentiated in reverse mode at the speed they solve, and the solvers built for differentiation solve more slowly. No single tool has yet offered a reverse-mode gradient at the speed of a fused-kernel solve. We present GRADSOLVE, an open-source JAX library for solving and reverse-mode differentiating low-dimensional ODE ensembles on NVIDIA GPUs. It records the st...
| Authors | Alessio Spurio Mancini |
|---|---|
| Primary category | cs.MS |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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