Up to 1920 processors of a cluster of distributed shared memory machines at the NASA Ames Research Center are being used to simulate ocean circulation globally at horizontal resolutions of 1/4, 1/8, and 1/16-degree with the Massachusetts Institute of Technology General Circulation Model, a finite volume code that can scale to large numbers of processors. The study aims to understand physical processes responsible for skill improvements as resolution is increased and to gain insight into what resolution is sufficient for particular purposes. This paper focuses on the computational aspects of reaching the technical objective of efficiently performing these global eddy-resolving ocean simulations. At 1/16-degree resolution the model grid contains 1.2 billion cells. At this resolution it is possible to simulate approximately one month of ocean dynamics in about 17 hours of wallclock time with a model timestep of two minutes on a cluster of four 512-way NUMA Altix systems. The Altix systems' large main memory and I/O subsystems allow computation and disk storage of rich sets of diagnostics during each integration, supporting the scientific objective to develop a better understanding of global ocean circulation model solution convergence as model resolution is increased.

Chris Hill

Dimitris Menemenlis

Bob Ciotti

Chris Henze

Investigating Solution Convergence in a Global Ocean Model Using a 2048-Processor Cluster of Distributed Shared Memory Machines

Robert C. Kirby

Understanding and Implementing the Finite Element Method, by Mark Gockenbach

Autonomous rover navigation is a critical technology for robotic exploration of Mars. Simulation allows more extensive testing of such technologies than would be possible with hardware test beds alone. A large number of simulations, running in parallel, can test an algorithm under many different operating conditions to quickly identify the operational envelope of the technology and identify failure modes that were not discovered in more limited testing. GESTALT is the autonomous navigation algorithm developed for NASA's Mars rovers. ROAMS is a rover simulator developed to support the Mars program. We have integrated GESTALT into ROAMS to test closed-loop, autonomous navigation in simulation. We have developed a prototype capability to run many copies of ROAMS in parallel on a supercomputer, varying input parameters to rapidly explore GESTALT's performance across a parameter space. Using these tools, we have demonstrated that large scale simulation can identify performance limits and unexpected behaviors in an algorithm. Such parallel simulation was able to test approximately 500 parameter combinations in the time required for a single test on a hardware test bed.

Richard Madison

Abhinandan Jain

Christopher Lim

Mark Maimone

Performance Characterization of a Rover Navigation Algorithm Using Large-Scale Simulation

Charles D. Norton

High Performance Computing for Mission-Enabling Space Applications

A parallel, three-dimensional electrostatic PIC code is developed for large-scale electric propulsion simulations using parallel supercomputers. This code uses a newly developed immersed-finite-element particle-in-cell (IFE-PIC) algorithm designed to handle complex boundary conditions accurately while maintaining the computational speed of the standard PIC code. Domain decomposition is used in both field solve and particle push to divide the computation among processors. Two simulations studies are presented to demonstrate the capability of the code. The first is a full particle simulation of near-thruster plume using real ion to electron mass ratio. The second is a high-resolution simulation of multiple ion thruster plume interactions for a realistic spacecraft using a domain enclosing the entire solar array panel. Performance benchmarks show that the IFE-PIC achieves a high parallel efficiency of &#x2265; 90&#x25;

Joseph Wang

Yong Cao

Raed Kafafy

Viktor Decyk

Electric Propulsion Plume Simulations Using Parallel Computer

The Spitzer Science Center's MOPEX software is a part of the Spitzer Space Telescope's operational pipeline that enables detection of cosmic ray collisions with the detector array, masking of the corrupted pixels due to these collisions, subsequent mosaicking of image fields, and extraction of point sources to create catalogs of celestial objects. This paper reports on our experiences in parallelizing the parts of MOPEX related to cosmic ray rejection and mosaicking on a 1,024-processor cluster computer at NASA's Jet Propulsion Laboratory. The architecture and performance of the new Parallel MOPEX software are described. This work was done in order to rapidly mosaic the IRAC shallow survey data, covering a region of the sky observed with one of Spitzer's infrared instruments for the study of galaxy clusters, large-scale structure, and brown dwarfs.

Joseph C. Jacob

Peter Eisenhardt

David Makovoz

Parallel MOPEX: Computing Mosaics of Large-Area Spitzer Surveys on a Cluster Computer

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Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

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Chief Editor&nbsp;Professor Tramontana&nbsp;is based at the University of Catania and his research primarily concerns the areas of software engineering and distributed systems.

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Scientific Programming

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Scientific Programming

High Performance Computing for Mission-Enabling Space Applications

High Performance Computing for Mission-Enabling Space Applications

Articles

Investigating Solution Convergence in a Global Ocean Model Using a 2048-Processor Cluster of Distributed Shared Memory Machines

Understanding and Implementing the Finite Element Method, by Mark Gockenbach

Performance Characterization of a Rover Navigation Algorithm Using Large-Scale Simulation

High Performance Computing for Mission-Enabling Space Applications

Electric Propulsion Plume Simulations Using Parallel Computer

Parallel MOPEX: Computing Mosaics of Large-Area Spitzer Surveys on a Cluster Computer