CSPlib: A performance portable parallel software toolkit for analyzing complex kinetic mechanisms

Díaz-Ibarra, Oscar Homero; Kim, Kyungjoo; Safta, Cosmin; Najm, Habib N.

doi:10.1016/j.cpc.2023.109069

Title: CSPlib: A performance portable parallel software toolkit for analyzing complex kinetic mechanisms

Journal Article · 26 December 2023 · Computer Physics Communications

DOI: https://doi.org/10.1016/j.cpc.2023.109069 · OSTI ID:2311381

^[1]; Kim, Kyungjoo ^[1]; Safta, Cosmin ^[2]; Najm, Habib N. ^[3]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research
Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis
Sandia National Lab. (SNL-CA), Livermore, CA (United States). Combustion Research Facility

Computational singular perturbation (CSP) is a method to analyze dynamical systems. It targets the decoupling of fast and slow dynamics using an alternate linear expansion of the right-hand side of the governing equations based on eigenanalysis of the associated Jacobian matrix. This representation facilitates diagnostic analysis, detection and control of stiffness, and the development of simplified models. For this work, we have implemented CSP in a C++ open-source library CSPlib using the Kokkos parallel programming model to address portability across diverse heterogeneous computing platforms, i.e., multi/many-core CPUs and GPUs. We describe the CSPlib implementation and present its computational performance across different computing platforms using several test problems. Specifically, we test the CSPlib performance for a constant pressure ignition reactor model on different architectures, including IBM Power 9, Intel Xeon Skylake, and NVIDIA V100 GPU. The size of the chemical kinetic mechanism is varied in these tests. As expected, the Jacobian matrix evaluation, the eigensolution of the Jacobian matrix, and matrix inversion are the most expensive computational tasks. When considering the higher throughput characteristic of GPUs, GPUs performs better for small matrices with higher occupancy rate. CPUs gain more advantages from the higher performance of well-tuned and optimized linear algebra libraries such as OpenBLAS.

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Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)

Grant/Contract Number:: NA0003525

OSTI ID:: 2311381

Alternate ID(s):: OSTI ID: 2369133

Report Number(s):: SAND--2024-00111J

Journal Information:: Computer Physics Communications, Journal Name: Computer Physics Communications Vol. 297; ISSN 0010-4655

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Title: CSPlib: A performance portable parallel software toolkit for analyzing complex kinetic mechanisms

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