Toward Performance-Portable PETSc for GPU-based Exascale Systems

TL;DR

This paper presents a design for PETSc that achieves performance portability across various GPU programming models, enabling scalable scientific computations on exascale systems.

Contribution

It introduces a flexible, extensible PETSc architecture that supports multiple GPU programming models, facilitating performance portability and ease of use for application developers.

Findings

High performance achieved on current GPU systems

Supports multiple programming models like CUDA, HIP, SYCL, Kokkos, RAJA, OpenCL

Demonstrates flexibility and scalability in case studies

Abstract

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.