|
|
||
|
|
|
|
|
|||
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
Proof
is in the Numbers
Before applying CAPO to GCEM3D, the cloud modeling code was only able
to run very small cases, scaling up to four processors on a PC. After
applying CAPO and making some adjustments, Jin and Jost achieved a factor
of 12 speed-up, when running a test case on 16 processors of an SGI Origin
3000 (see figure, right). Using larger cases, the code scaled up to 64
CPUs.
The newly optimized GCEM3D code enabled Goddard researchers to increase
the resolution of their case studies. They successfully ran a large test
case of 1,026-by-1,026-by-34, using more than seven gigabytes of memory—a
new feat using this application. "Our goal of cloud modeling not
only aims to better understand the microphysical and dynamical processes
of the cloud system itself, but also to improve their representation for
large-scale applications, such as studies on the precipitating convective
system, air-sea interactions and cloud-aerosol interactions, as well as
the global change in climate and hydrology," explains Shie. CAPO
enables NASA Goddard scientists to achieve these research goals much faster.
After the success with their cloud modeling code, researchers at Goddard
are now interested in applying the CAPO tool to other serial codes. "I
will apply CAPO to other codes in the future because of the substantial
improvement in model performance due to computational efficiency and memory
extension," says Shie.
Tao and Shie visited NASA Ames in September 2002 to learn more about CAPO. And Jin and Jost visited Goddard to demonstrate the tool to a group of researchers. The CAPO team is aiming is to transfer knowledge of the tool so that individual users can apply the tool to their codes. For decades, NASA scientists have been generating serial codes which at this point need to be parallelized. Thanks to CAPO, those codes can now run more efficiently on the agency’s shared-memory parallel systems like those at the NAS Facility. CAPO was first developed in 1998, and inspired by a collaboration with the creators of CAPTools at the University of Greenwich in the United Kingdom. CAPTools (now marketed as ParaWise) is software that generates code to run on distributed-memory machines. Look for the full story, "Parallelization—the Key to Faster Codes, Higher Fidelity Simulations," by NAS staff writer Holly A. Amundson, in the Winter 2003 issue of Gridpoints.
| ||||||
|
||||||
