Multi-core acceleration of chemical kinetics for simulation and prediction

This work implements a computationally expensive chemical kinetics kernel from a large-scale community atmospheric model on three multi-core platforms: NVIDIA GPUs using CUDA, the Cell Broadband Engine, and Intel Quad-Core Xeon CPUs. A comparative performance analysis for each platform in double and...

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Veröffentlicht in:Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis S. 1 - 11
Hauptverfasser: Linford, John C., Michalakes, John, Vachharajani, Manish, Sandu, Adrian
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 14.11.2009
Schriftenreihe:ACM Conferences
Schlagworte:
Applied computing > Physical sciences and engineering
Atmospheric modeling
cell broadband engine
chemical kinetics
Chemical reactions
Chemicals
Codes
Computational modeling
Computer architecture
Computer systems organization > Architectures > Parallel architectures > Multiple instruction, multiple data
Computing methodologies > Concurrent computing methodologies > Concurrent programming languages
Graphics processing units
kinetic preprocessor
Kinetic theory
Mathematical models
multi-core
NVIDIA CUDA
open-MP
Software and its engineering > Software notations and tools > General programming languages > Language types > Concurrent programming languages
Vectors
multi-core
open-MP
chemical kinetics
NVIDIA CUDA
kinetic preprocessor
atmospheric modeling
cell broadband engine
ISBN:1605587443, 9781605587448
ISSN:2167-4329
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This work implements a computationally expensive chemical kinetics kernel from a large-scale community atmospheric model on three multi-core platforms: NVIDIA GPUs using CUDA, the Cell Broadband Engine, and Intel Quad-Core Xeon CPUs. A comparative performance analysis for each platform in double and single precision on coarse and fine grids is presented. Platform-specific design and optimization is discussed in a mechanism-agnostic way, permitting the optimization of many chemical mechanisms. The implementation of a three-stage Rosenbrock solver for SIMD architectures is discussed. When used as a template mechanism in the the Kinetic PreProcessor, the multi-core implementation enables the automatic optimization and porting of many chemical mechanisms on a variety of multi-core platforms. Speedups of 5.5x in single precision and 2.7x in double precision are observed when compared to eight Xeon cores. Compared to the serial implementation, the maximum observed speedup is 41.1x in single precision.
ISBN:1605587443
9781605587448
ISSN:2167-4329
DOI:10.1145/1654059.1654067