# Toward Performance-Portable PETSc for GPU-based Exascale Systems

arXiv: https://arxiv.org/abs/2011.00715 · HTML: https://mlsanity.com/paper/2011.00715/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [PETScML: Second-order solvers for training regression problems in Scientific Machine Learning](https://mlsanity.com/paper/2403.12188/) (2024)
- [A memory-efficient neural ODE framework based on high-level adjoint differentiation](https://mlsanity.com/paper/2206.01298/) (2022)
- [Semi-Implicit Neural Ordinary Differential Equations](https://mlsanity.com/paper/2412.11301/) (2024)
- [Automated Grammar-based Algebraic Multigrid Design With Evolutionary Algorithms](https://mlsanity.com/paper/2603.17641/) (2026)
- [Gleam: Adaptive Network-Efficient CUDA API Remoting for Cross-Device GPU Sharing over LANs](https://mlsanity.com/paper/2607.23115/) (2026)

## Abstract (arXiv)

The Portable Extensible Toolkit for Scientific computation (PETSc) library delivers scalable solvers for nonlinear time-dependent differential and algebraic equations and for numerical optimization.The PETSc design for performance portability addresses fundamental GPU accelerator challenges and stresses flexibility and extensibility by separating the programming model used by the application from that used by the library, and it enables application developers to use their preferred programming model, such as Kokkos, RAJA, SYCL, HIP, CUDA, or OpenCL, on upcoming exascale systems. A blueprint for using GPUs from PETSc-based codes is provided, and case studies emphasize the flexibility and high performance achieved on current GPU-based systems.