JAX-AMG: New GPU-Accelerated Library for Solving Sparse Linear Systems with Automatic Differentiation
Researchers have released JAX-AMG, a new software library that combines GPU acceleration, algebraic multigrid solving, and automatic differentiation for sparse linear systems used in scientific computing. The library wraps Nvidia's AmgX solver and integrates with JAX's ecosystem, supporting distributed multi-GPU execution and reverse-mode automatic differentiation. This addresses a gap in existing tools and enables more efficient PDE-constrained optimization and inverse problem solving.
JAX-AMG is a new library that solves a previously unmet need in scientific computing by combining three key capabilities: GPU acceleration, algebraic multigrid (AMG) methods, and automatic differentiation. The library wraps Nvidia's AmgX solver suite as a native JAX primitive, providing access to AMG and Krylov methods with configurable preconditioners through a unified interface. It supports JIT compilation, reverse-mode automatic differentiation via adjoint methods, batched solves, and MPI-based distributed execution across multiple GPUs. A solver caching mechanism reduces computational overhead by amortizing setup costs across repeated solves, making the library practical for computationally intensive applications. The integration into JAX's ecosystem enables seamless use in differentiable simulation and scientific machine learning pipelines.
What's missing
The paper does not provide performance benchmarks comparing JAX-AMG to existing solvers, nor does it discuss specific limitations of the adjoint method approach for certain problem classes or convergence guarantees for the AMG preconditioner configurations.
What different sources said
- arXiv physicsCenter
JAX-AMG: A GPU-Accelerated Differentiable Sparse Linear Solver Library for JAX
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