For most of us, cloud watching is a good way to decide whether to grab an umbrella, or to imagine birds, whales, and elephants in the sky. For NASA researchers, studying clouds helps shed light on phenomena such as air-sea interactions and global climate changes. To create better cloud models, researchers at NASA Goddard Space Flight Center, Greenbelt, Md., are using a computer code optimized by NAS Division researchers Henry Jin and Gabriele Jost.

Jin and Jost have worked with Goddard scientists Wei-Kuo Tao, Dan Johnson, and Chung-Lin Shie to improve the three-dimensional Goddard Cumulus Ensemble (GCEM3D) Code. To do this, the NAS researchers applied their tool, called Computer-Aided Parallelization and Optimizer (CAPO), to the GCEM3D code. CAPO is designed to take advantage of shared-memory parallel computers, and automates the labor-intensive steps of parallelizing serial code. "I think the CAPO tool is very useful, especially for model runs demanding lots of processors and massive amounts of memory," says Shie.

Parallelizing the code enabled the Goddard scientists to run larger cloud simulations much faster than before. "The idea is, that if it takes x hours to run a simulation on a single processor it will only take a fraction of x to run it on multiple processors," explains Jost.