DaCe AD
Unifying high-performance automatic differentiation for ML and scientific computing
DaCe AD: Unifying High-Performance Automatic Differentiation for ML and Scientific Computing
An efficient automatic differentiation framework that, on average, is over 92× faster than JAX on gradient computation for scientific computing patterns.
Benchmarked on the NPBench suite. Unifies AD across ML and scientific computing workloads in a single framework.
- Role: Lead author
- Methods: DaCe, automatic differentiation, NPBench
- Status: Published, CLUSTER 2025
- Paper: arXiv:2509.02197