Abstract
Performance tests and analyses are critical to effective high-performance computing software development and are central components in the design and implementation of computational algorithms for achieving faster simulations on existing and future computing architectures for large-scale application problems. In this article, we explore performance and space-time trade-offs for important compute-intensive kernels of large-scale numerical solvers for partial differential equations (PDEs) that govern a wide range of physical applications. We consider a sequence of PDE-motivated bake-off problems designed to establish best practices for efficient high-order simulations across a variety of codes and platforms. We measure peak performance (degrees of freedom per second) on a fixed number of nodes and identify effective code optimization strategies for each architecture. In addition to peak performance, we identify the minimum time to solution at 80% parallel efficiency. The performance analysis is based on spectral and p-type finite elements but is equally applicable to a broad spectrum of numerical PDE discretizations, including finite difference, finite volume, and h-type finite elements.
Original language | American English |
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Pages (from-to) | 562-586 |
Number of pages | 25 |
Journal | International Journal of High Performance Computing Applications |
Volume | 34 |
Issue number | 5 |
DOIs | |
State | Published - 2020 |
NREL Publication Number
- NREL/JA-2C00-77682
Keywords
- high-order discretizations
- high-performance computing
- PDEs
- strong-scale limit