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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| Vydané v: | Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis s. 1 - 11 |
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| Hlavní autori: | , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
New York, NY, USA
ACM
14.11.2009
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| Edícia: | ACM Conferences |
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| ISBN: | 1605587443, 9781605587448 |
| ISSN: | 2167-4329 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Vachharajani, Manish Michalakes, John Linford, John C. Sandu, Adrian |
| Author_xml | – sequence: 1 givenname: John C. surname: Linford fullname: Linford, John C. organization: Virginia Polytechnic Institute and State University, Blacksburg, VA – sequence: 2 givenname: John surname: Michalakes fullname: Michalakes, John organization: National Center for Atmospheric Research, Boulder, CO – sequence: 3 givenname: Manish surname: Vachharajani fullname: Vachharajani, Manish organization: University of Colorado at Boulder, Boulder, CO – sequence: 4 givenname: Adrian surname: Sandu fullname: Sandu, Adrian organization: Virginia Polytechnic Institute and State University, Blacksburg, VA |
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| Keywords | multi-core open-MP chemical kinetics NVIDIA CUDA kinetic preprocessor atmospheric modeling cell broadband engine |
| Language | English |
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| Snippet | This work implements a computationally expensive chemical kinetics kernel from a large-scale community atmospheric model on three multi-core platforms: NVIDIA... |
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| SubjectTerms | 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 |
| Title | Multi-core acceleration of chemical kinetics for simulation and prediction |
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