Toward Performance-Portable PETSc for GPU-based Exascale Systems
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 0; 2026: 2.
5 papers.
- 2024PETScML: Second-order solvers for training regression problems in Scientific Machine Learningcited by 4
- 2022A memory-efficient neural ODE framework based on high-level adjoint differentiationcited by 3
- 2024Semi-Implicit Neural Ordinary Differential Equationscited by 2
- 2026Automated Grammar-based Algebraic Multigrid Design With Evolutionary Algorithmscited by 0
- 2026Gleam: Adaptive Network-Efficient CUDA API Remoting for Cross-Device GPU Sharing over LANscited by 0
- 2026Gleam: Adaptive Network-Efficient CUDA API Remoting for Cross-Device GPU Sharing over LANs25 Jul 2026
- 2026Automated Grammar-based Algebraic Multigrid Design With Evolutionary Algorithms18 Mar 2026
- 2024Semi-Implicit Neural Ordinary Differential Equations15 Dec 2024
- 2024PETScML: Second-order solvers for training regression problems in Scientific Machine Learning18 Mar 2024
- 2022A memory-efficient neural ODE framework based on high-level adjoint differentiation2 Jun 2022