Presentations
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Scientific Software and Test Driven Development
2012.06.05
While test driven development (TDD) is gaining wide acceptance in many professional software communities, numerical algorithms present several unique challenges that have cast some doubt on the relevance of the methodology to the development of scientific software. Such concerns range from the need for reliable error estimates for roundoff and truncation errors to the general lack of known analytic solutions for codes designed for fundamental research. Fortunately, approaches can be found that minimize and/or circumvent these issues.
Presented by: Dr. Tom Clune
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GMAO/GEOS-5 Data Production Overview
2010.08.03
An overview of the GEOS-5 atmospheric assimilation and forecast data production by 610.1, that includes NRT operations for EOS instrument teams and field campaign support as well as long-term reanalysis for global user community.
Presentation by: Dr. Gi-Kong Kim
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NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA)
2009.09.28
Presentation to the Department of Earth and Atmospheric Sciences
Presentation by: Dr. Michael Bosilovich
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Accelerating Climate Models with the IBM Cell Processor
2008.12.16
Ever increasing model resolutions and physical processes in climate models demand continual computing power increases. The IBM Cell processor's order-of- magnitude peak performance increase over conventional processors makes it very attractive for fulfilling this requirement. However, the Cell's characteristics: 256KB local memory per SPE and the new low-level communication mechanism, make it very challenging to port an application. We selected the solar radiation component of the NASA GEOS-5 climate model, which: (1) is representative of column physics components (~50% total computation time), (2) has a high computational load relative to data traffic to/from main memory, and (3) performs independent calculations across multiple columns. We converted the baseline code (single-precision, Fortran code) to C and ported it to an IBM BladeCenter QS20, manually SIMDizing 4 independent columns, and found that a Cell with 8 SPEs can process more than 3000 columns per second. Compared with the baseline results, the Cell is ~6.76x, ~8.91x, ~9.85x faster than a core on Intel's Xeon Woodcrest, Dempsey, and Itanium2 respectively. Our analysis shows that the Cell could also speed up the dynamics component (~25% total computation time). We believe this dramatic performance improvement makes the Cell processor very competitive, at least as an accelerator. We will report our experience in porting both the C and Fortran codes and will discuss our work in porting other climate model components.
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