Automatic Generation of Multicore Chemical Kernels

John C. Linford, John Michalakes, Manish Vachharajani, Adrian Sandu
Virginia Polytechnic Institute and State University, Blacksburg
IEEE Transactions on Parallel and Distributed Systems, 2011, Volume: 22, Issue: 1, p.119-131


   title={Automatic Generation of Multi-Core Chemical Kernels},

   author={Linford, J.C. and Michalakes, J. and Vachharajani, M. and Sandu, A.},

   journal={IEEE Transactions on Parallel and Distributed Systems},



   publisher={Published by the IEEE Computer Society}


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This work presents the Kinetics Preprocessor: Accelerated (KPPA), a general analysis and code generation tool that achieves significantly reduced time-to-solution for chemical kinetics kernels on three multicore platforms: NVIDIA GPUs using CUDA, the Cell Broadband Engine, and Intel Quad-Core Xeon CPUs. A comparative performance analysis of chemical kernels from WRFChem and the Community Multiscale Air Quality Model (CMAQ) is presented for each platform in double and single precision on coarse and fine grids. We introduce the multicore architecture parameterization that KPPA uses to generate a chemical kernel for these platforms and describe a code generation system that produces highly tuned platform-specific code. Compared to state-of-the-art serial implementations, speedups exceeding 25x are regularly observed, with a maximum observed speedup of 41.1x in single precision.
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