optimizing large scale chemical transport models for multicore platforms
The performance of a typical chemical transport model is determined on two multicore processors: the heterogeneous Cell Broadband Engine and the homogeneous Intel Quad-Core Xeon shared-memory multiprocessor. Two problem decomposition techniques are discussed: dimension splitting for promoting parallelization in chemical transport models, and time splitting, for reducing truncation error. Additionally, a scalable method for accessing random rows or columns of a matrix of arbitrary size from the accelerator units of the Cell Broadband Engine is presented. This scalable access method increases chemical transport model efficiency by an average of 30\% and significantly improves the scalability of dimension-splitting techniques on the Cell Broadband Engine. Experiments show that chemical transport models are 31\% more efficient on the Cell Broadband Engine when only six accelerator units are used than on a shared-memory multiprocessor with eight executing cores. Our fully-optimized models achieve an average 118\% speedup on the Cell Broadband Engine, and an average 87.5\% speedup on a shared-memory multiprocessor with OpenMP.
chemical transport model multicore intel quad-core xeon time splitting scalability domain decomposition ibm cell broadband engine