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Proceedings Paper

Intel Many Integrated Core (MIC) architecture optimization strategies for a memory-bound Weather Research and Forecasting (WRF) Goddard microphysics scheme
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Paper Abstract

The Goddard cloud microphysics scheme is a sophisticated cloud microphysics scheme in the Weather Research and Forecasting (WRF) model. The WRF is a widely used weather prediction system in the world. It development is a done in collaborative around the globe. The Goddard microphysics scheme is very suitable for massively parallel computation as there are no interactions among horizontal grid points. Compared to the earlier microphysics schemes, the Goddard scheme incorporates a large number of improvements. Thus, we have optimized the code of this important part of WRF. In this paper, we present our results of optimizing the Goddard microphysics scheme on Intel Many Integrated Core Architecture (MIC) hardware. The Intel Xeon Phi coprocessor is the first product based on Intel MIC architecture, and it consists of up to 61 cores connected by a high performance on-die bidirectional interconnect. The Intel MIC is capable of executing a full operating system and entire programs rather than just kernels as the GPU do. The MIC coprocessor supports all important Intel development tools. Thus, the development environment is familiar one to a vast number of CPU developers. Although, getting a maximum performance out of MICs will require using some novel optimization techniques. Those optimization techniques are discusses in this paper. The results show that the optimizations improved performance of the original code on Xeon Phi 7120P by a factor of 4.7x. Furthermore, the same optimizations improved performance on a dual socket Intel Xeon E5-2670 system by a factor of 2.8x compared to the original code.

Paper Details

Date Published: 21 October 2014
PDF: 9 pages
Proc. SPIE 9247, High-Performance Computing in Remote Sensing IV, 924704 (21 October 2014); doi: 10.1117/12.2068358
Show Author Affiliations
Jarno Mielikainen, Univ. of Wisconsin-Madison (United States)
Bormin Huang, Univ. of Wisconsin-Madison (United States)
Allen H.-L. Huang, Univ. of Wisconsin-Madison (United States)

Published in SPIE Proceedings Vol. 9247:
High-Performance Computing in Remote Sensing IV
Bormin Huang; Sebastian López; Zhensen Wu, Editor(s)

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